This page tracks remote llm engineer openings that are location-eligible for Kansas.
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We support Swiss SMEs in their international business and help innovative foreign companies to establish in Switzerland.
• Lead AI/LLM strategy, solution architecture, and implementation across Engineering, Operations, and Project Delivery. • Build and maintain LLM-based agents to support: intelligent processing of technical documentation, automated design validation and engineering workflows, testing and QA automation, knowledge retrieval and contextual reasoning. • Integrate AI into core power automation workflows: IEC 61850 SCD engineering files, relay settings, SCADA HMI & logic, substation documentation, etc. • Establish AI governance, secure data pipelines, and compliance with utility-grade cybersecurity standards. • Partner with engineering managers and subject-matter experts to identify high-value AI automation opportunities. • Develop scalable pipelines for inference, fine-tuning, continuous learning, and lifecycle management in cloud and on-prem environments. • Evaluate and incorporate emerging AI technologies (RAG, vector stores, autonomous agents, internal copilots). • Monitor model performance, accuracy, drift, and cost; lead improvement cycles and risk mitigation. • Train and coach engineering teams on practical AI tools and adoption in daily workflows. • Ensure compliance with GE Vernova global standards, regulatory expectations, and utility-sector requirements.
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. We recognize that our people are our strength. We are an equal opportunity employer and place a high value on diversity and inclusion. We do not discriminate on the basis of any protected attribute. We make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans.
Role Description We are looking for an LLM Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Key Responsibilities - Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques. - Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data. - Build scalable training pipelines on top of modern distributed training frameworks. - Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning. - Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods. - Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes. - Implement safety, refusal, and policy evaluations to track model behavior across releases. - Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably. - Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations. - Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments. - Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs. - Document training methodology, results, and decisions clearly for technical and non-technical audiences. - Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment. - Stay current with LLM research and translate advances into production-ready fine-tuning recipes. Qualifications - Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience. - Six or more years of combined ML research and engineering experience, with significant LLM exposure. - Strong proficiency in Python and modern deep learning frameworks, especially PyTorch. - Hands-on experience fine-tuning transformer-based language models at non-trivial scale. - Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism. - Experience with RLHF, DPO, or other preference optimization techniques. - Strong understanding of evaluation methodology, benchmarks, and human evaluation design. - Experience operating training jobs on GPU clusters and recovering from failures. - Strong written and verbal communication skills. - Track record of shipping or publishing impactful LLM work. Preferred Qualifications - Publications at top-tier ML venues. - Experience with multimodal model fine-tuning. - Familiarity with synthetic data generation and dataset distillation. - Open-source contributions to LLM training libraries. - Exposure to responsible AI evaluation and red-teaming practices. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com .
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• Design and maintain a safety evaluation framework—adversarial prompt sets, scenario-based test suites, and regression suites—so that every model and agent update is validated before it ships. • Lead structured red-teaming exercises covering jailbreaks, prompt injection, tool misuse, and data exfiltration; document findings and drive each issue through to remediation and closure. • Build and iterate on guardrail logic, including input/output filtering, tool-boundary constraints, action validation, sensitive-data redaction, and policy prompting. • Integrate safety checks into CI/CD and runtime so that unsafe behavior is intercepted before it reaches users. • Perform threat modeling for agentic scenarios: tool-call boundaries, sandbox isolation, and least-privilege access, with particular attention to preventing agents from exfiltrating data or executing irreversible actions through chained tool calls. • Conduct safety reviews of reinforcement-learning (RL) environments and trajectory data, partnering with environment and agent engineering teams to embed safety constraints directly into the environments themselves. • Instrument AI features for safety with structured logging, tracing, and metrics, enabling detection of unsafe patterns and regressions in production. • Prepare evidence for governance reviews—test reports, evaluation summaries, and mitigation validation—aligned with internal Responsible AI standards. • Collaborate with Product and UX to improve safety interactions (warnings, confirmations, refusal messaging, and feedback collection), and align evaluation goals with the Research and Data teams.
• Design and develop AI applications using Amazon Bedrock • Build and configure Bedrock Agents and Action Groups • Implement Retrieval-Augmented Generation (RAG) architectures • Develop natural-language-to-API workflows • Configure Bedrock Knowledge Bases and vector search solutions • Implement AI safety controls, content filtering, and PII protection • Build and maintain serverless AWS solutions • Deploy infrastructure using Infrastructure-as-Code practices • Monitor AI system performance, usage, and operational metrics
Vultr is on a mission to make high-performance cloud computing easy to use, affordable, and locally accessible.
• Strategic Account Ownership • Drive AI Infrastructure Revenue • Customer-Centric Engagement • Trusted Advisor • Collaborate for Success • Sales Process Excellence & Operational Hygiene • AI Ecosystem Engagement
We securely connect everything to make anything possible.
Role Description Cisco is currently seeking two senior product marketing leaders to join our team. These are remote roles based in the United States, requiring significant travel (approximately 30–50% of the time). In these roles, you will lead the product marketing strategy for Cisco’s AI infrastructure portfolio, connecting technical capabilities to the business outcomes customers require for AI workloads and AI-native applications. You will: - Define the narrative, positioning, messaging, launch strategy, and sales enablement required to power secure, connected, AI-ready organizations. - Partner with Product Management, Engineering, Sales, Corporate Marketing, Analyst Relations, and Communications. - Translate sophisticated infrastructure innovation into market value. - Drive cross-functional alignment. - Equip the business to win with enterprise buyers and executives. Qualifications - Bachelor’s degree in Business, Engineering, Marketing, Communications, or a related field, or 19+ years of equivalent professional experience. - 15+ years of experience in B2B technology marketing. - 5+ years of experience in product marketing leadership specifically within enterprise infrastructure, networking, data center, cloud, security, or AI/ML platforms. - 5+ years of experience developing product positioning, messaging, go-to-market strategy, launch plans, and sales enablement assets for enterprise technology solutions. Requirements - Ability to travel domestically and internationally up to 50% of the time. - Experience working directly with product and engineering teams to translate technical roadmaps into customer-facing value propositions. - Experience leading cross-functional initiatives involving sales, marketing, and executive stakeholders. - Proficiency in utilizing market data, customer insights, and campaign performance metrics to report on strategy effectiveness. - Experience in creating and executing go-to-market strategies for category-defining technology. - Direct experience managing and influencing distributed, global teams. - Demonstrated history of delivering thought leadership content, including white papers, executive narratives, and solution briefs for technical audiences. - Experience building and executing enablement programs for global sales organizations and channel partners. - Advanced degree (MBA or equivalent) in a relevant field. Benefits - Medical, dental and vision insurance. - 401(k) plan with a Cisco matching contribution. - Paid parental leave. - Short and long-term disability coverage. - Basic life insurance. - 10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees. - 1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco. - 16 days of paid vacation time per full calendar year for non-exempt employees. - Flexible vacation time off program for exempt employees. - 80 hours of sick time off provided on hire date and each January 1st thereafter. - Optional 10 paid days per full calendar year to volunteer.
We give children a healthy start in life, the opportunity to learn and protection from harm in the US & around the world
Title: Managing Director, Campaign Strategy & Infrastructure (M3) Location: United States remote Job Category: Advocacy Campaigns & Engagement Requisition Number: MANAG008221 Full-Time Job Description: Save the Children Action Network Save the Children Action Network ("SCAN") - a 501(c)(4) organization - is the political advocacy arm of Save the Children. We are building bipartisan support to make sure every child has a strong start in life. We're doing this by advocating statewide and federally for high-quality early learning and ending child hunger in the U.S., the safety of children arriving at the southern U.S. border and educating and protecting kids around the world. The Role As the Managing Director, Campaign Strategy & Infrastructure, you will play a critical role in supporting the Head of SCAN in advancing SCAN's mission and strategic priorities. Working in close partnership with the Head, you will contribute to the development and execution of SCAN's campaign strategy, with a particular focus on building the infrastructure, systems, and tools required to run effective advocacy, electoral, and community-based campaigns in states nationwide. You will lead the design and implementation of scalable campaign frameworks, training programs, and operational systems that enable staff, volunteers, and grassroots advocates to mobilize effectively and work in a coordinated, high-impact way. In collaboration with the Head, Regional Directors and cross-functional partners, you will ensure campaigns are strategically aligned, well-executed, and positioned to drive engagement and influence policy outcomes at both the state and federal levels. Your work will be central to strengthening organizational cohesion, improving campaign effectiveness, and translating strategy into execution across SCAN's advocacy and electoral efforts. Location Hybrid - Washington DC, Fairfield, CT, Lexington, KY office locations Remote - United States What You'll Be Doing (Essential Duties) - not inclusive of all role responsibilities. May be subject to change. Campaign Strategy & Infrastructure Leadership (30%) - Support the development and operationalization of SCAN's campaign strategy by designing and continuously improving systems and ways of working. - Build and manage scalable campaign infrastructure-including planning tools, standards, and operating procedures-to enable consistent, high-quality execution across teams. - Coordinate closely with Campaigns, State, Elections, Constituency Engagement, and Data teams to align priorities, sequencing, and resource allocation in support of strategic objectives - Establish processes to evaluate campaign performance, capture learnings, and strengthen long-term organizing and advocacy effectiveness. - Support campaign readiness and rapid-response capabilities during key advocacy and electoral moments, ensuring teams are equipped to mobilize effectively. Team Leadership & Operational Management (25%) - Lead, develop, and coach a high-performing team - Set clear goals, performance expectations, and accountability frameworks aligned with the Head's strategic priorities and overall organizational outcomes - Foster a collaborative, inclusive, and high-performing team culture that prioritizes integration, innovation, and continuous improvement - Represent SCAN in cross-divisional initiatives and external engagements, reinforcing alignment with strategic objectives and elevating campaign impact - Support fundraising, donor engagement, and strategic communications by clearly articulating SCAN's organizing model, campaign approach, and measurable results Training, Leadership Development & Organizing Capacity Building (25%) - Lead SCAN's approach to building organizing capacity across staff, volunteers, and partners. - Oversee the design and delivery of training programs that strengthen capabilities across advocacy, elections, volunteer engagement, and community organizing. - Ensure training curricula and learning experiences build durable organizing skills and develop a strong pipeline of grassroots and organizational leaders. - Partner with internal teams and external organizations to expand training partnerships, represent SCAN in key forums, and support shared learning initiatives. - Develop systems for knowledge sharing and continuous learning, enabling best practices and consistent approaches across campaign teams. Cross-Functional Integration & Organizational Alignment (20%) - Partner with senior leaders across divisions to ensure campaign infrastructure and engagement strategies are aligned with broader organizational priorities, including policy, programs, and fundraising. - Facilitate cross-functional planning processes that integrate constituency engagement, volunteer mobilization, electoral activity, and advocacy priorities. - Work closely with programmatic teams to align SCAN's campaign presence with programmatic state footprints, ensuring coordinated, mutually reinforcing impact. - Translate campaign insights and operational data into strategic reporting, campaign readiness assessments, and recommendations to inform organizational decision-making. Required qualifications for the role - Minimum of a bachelor's degree or equivalent experience, plus at least 10 years of relevant experience on Capitol Hill, in non-profit advocacy, campaign organizing - Demonstrated experience leading complex, multi-channel advocacy, electoral, grassroots, or organizing initiatives - Proven effective leader and people manager with experience developing and leading high performance cross functional teams - Proven ability to create effective grassroots campaigns - Experience creating or managing training programs, leadership growth, volunteer involvement, or building organizing skills - Strong strategic planning, operational management, and cross-functional collaboration skills - Experience using data, metrics, and reporting to inform strategy and evaluate impact - Willingness and ability to travel up to 15-20% domestically - Professional proficiency in MS Office suite - Professional proficiency in spoken and written English Compensation Save the Children Action Network is offering the following salary ranges for this position, dependent on candidate location: - Geo 1 - NY Metro, DC, and other locations with labor costs significantly above national average: Target Salary for this position is $143,650 - $160,550 base salary - Geo 2 - Locations around the US National Labor Cost Average: Target Salary for this position is $130,900 - $146,300 base salary - Geo 3 - Locations significantly below the US National Labor Cost Average: Target Salary for this position is $116,875 - $130,625 base salary The salary ranges listed above are for US based candidates. For candidates located outside of the US, salary ranges will be based on the salary scales of the local employer of record. Actual base salary may vary based on, but not limited to, relevant experience, base salary of internal peers, business sector, and geographic location (more information on job structure is available here). About Us We are looking to build a diverse, equitable and inclusive team at Save the Children Action Network. We offer a range of outstanding benefits to support this goal: - Flexible schedules and time off: Flexible schedules, generous PTO, 11 paid holidays plus 2 floating holidays and hybrid working opportunities - Health: Competitive health care, dental and vision coverage for you and your family - Family: A variety of paid leaves: caregiver, parental/adoption, critical child illness and fertility benefits - Employee Rewards Program: Annual merit increases and/or additional incentives for eligible employees - /Retirement: A retirement savings plan with employer contributions (after one year) - Wellness: 15 safety and wellness days annually (if hired on or after July 1, safety and wellness days prorated to 8 days), mental health benefits and support through Calm and company-hosted events - Employee Assistance Program: free and confidential assessments, short-term counseling, referrals, and follow-up services - Learning & Growth: Access to internal and external learning & development opportunities and mentorships Click here to learn more about how Save the Children Action Network will invest in you. Save the Children Action Network is committed to conducting its programs and operations in a manner that is safe for the children it serves and helping protect the children with whom we are in contact. All Save the Children Action Network representatives are explicitly prohibited from engaging in any activity that may result in any kind of child abuse. Save the Children Action Network is committed to minimizing safety and security risks for our valued employees, ensuring all are given training, support and information to reduce their risk exposure while maximizing the impact of our programs for children and families. Our shared duty, both agency and individual, is to seek and maintain safe working conditions for all.
Vultr is on a mission to make high-performance cloud computing easy to use, affordable, and locally accessible.
• Own the discovery and definition of customer requirements for AI infrastructure use cases, including training, inference, GPU clusters, bare metal, managed orchestration, networking, and storage • Work directly with strategic customers to understand their technical needs, deployment timelines, workload patterns, and success criteria • Translate customer requirements into clear product requirements, technical specifications, and engineering priorities • Partner closely with engineering, infrastructure, networking, storage, and operations teams to deliver customer-ready solutions • Drive alignment across customer needs, product roadmap, architecture decisions, and delivery execution • Understand and define requirements across GPU compute, CPU, memory, local and shared storage, high-performance networking, cluster topology, and orchestration layers such as Kubernetes and Slurm • Support customer conversations around infrastructure design, capacity planning, performance expectations, operational readiness, and acceptance criteria • Help define product capabilities that can scale beyond a single customer into repeatable AI infrastructure offerings
• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management. • Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments. • Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation. • Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads. • Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability. • Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes. • Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment. • Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code. • Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them. • Support enterprise AI governance, compliance, auditability, and model risk management requirements. • Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems. • Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems. • Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies. • Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks. • Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability. • Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases. • Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics. • Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments. • Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications. • Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments. • Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows. • Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement. • Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives. • Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data. • Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.
Role Description Throughput. Latency. KV cache utilization. Move those three numbers in the right direction, and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every kernel you tune, every quantization scheme you ship, every scheduler tweak you land shows up directly in a customer's p99 and on our P&L. This is a high-impact seat. It is also a high-autonomy seat as you'll be given the room to lead the technical direction of inference optimization at Kimchi, not execute someone else's roadmap. The problem: running LLMs in production is a moving target. The "right" model and serving configuration for a workload depend on: - Traffic shape - Sequence-length distribution - Batch dynamics - GPU SKU - Memory bandwidth - Quantization tolerance - A dozen other variables that shift week to week Most teams pick a model once, over-provision GPUs, and absorb the cost. Kimchi is the system that makes that decision automatically - continuously matching workloads to the most cost-efficient, best-performing LLM and serving configuration on a customer's infrastructure. We're building the optimization layer between the model and the hardware, and we need engineers who understand both sides deeply. Qualifications - 5+ years building real ML systems, with a portfolio that shows depth in inference or training infrastructure (not just model training notebooks). - Strong Python - production services, not scripts. - Hands-on experience with at least one of vLLM, SGLang, or TensorRT-LLM, and a working mental model of why an inference engine performs the way it does on a given GPU. - Fluency with quantization tradeoffs - you've measured quality regressions, not just compression ratios. - Comfort with distributed systems: collective communication, sharding strategies, and the practical failure modes of multi-GPU and multi-node setups. - A bias toward measurement. You instrument before you optimize, and you can tell the difference between a real win and a benchmark artifact. - Self-direction. This role comes with a wide mandate; you should be excited by that, not unsettled by it. Requirements - Push throughput. Continuous batching, speculative decoding, chunked prefill, kernel-level tuning across vLLM, SGLang, and TensorRT-LLM. Find the ceiling on each GPU SKU, then raise it. - Cut latency. Attack TTFT and TPOT separately. Profile, identify the actual bottleneck (compute, memory bandwidth, scheduling, networking), and fix it - not the bottleneck you assumed. - Get more out of the KV cache. Paged attention, prefix caching, eviction policies, cache reuse across requests, quantized KV. This is where a lot of the unrealized throughput lives, and it's an area you'll own. - Quantize without regressing quality. INT8, INT4, FP8 across weights, activations, and KV. Empirical work: measure quality on real workloads, not just perplexity benchmarks. - Shrink cold starts and memory footprint. Faster init, smarter weight loading, tighter memory accounting - the difference between a model that scales and one that doesn't. - Scale across nodes. Distributed inference topologies, network-aware placement, checkpointing strategies that don't bottleneck on storage or interconnect. - Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments. Benefits - Competitive salary (depending on the level of experience). - Enjoy a flexible, remote-first global environment. - Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. - Equity options. - Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. - Spend 10% of your work time on personal projects or self-improvement. - Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. - Annual hackathon to spark new ideas and strengthen team bonds. - Team-building budget and company events to connect with your colleagues. - Equipment budget to ensure you have everything you need. - Extra days off to help maintain a healthy work-life balance. Hiring process - Screening call with Recruiter - Hiring Manager interview - Technical interview (system design) - Live coding - Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit.
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Cloud, Python, AWS, Cyber Security, Azure, Google Cloud Platform
