This page tracks remote llm engineer openings that are location-eligible for Massachusetts.
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• We are seeking a patient, articulate, and technically fluent Part-Time Technical Trainer to join our team on an on-demand basis. • This role sits closer to technical marketing than software development — you will need to understand and confidently use our GPUaaS platform and surrounding ecosystem, but you are not expected to develop on top of it. • Your primary focus will be two-fold: (1) building customer-centric demo content that replaces engineering-centric recordings with relatable, use-case-driven flows, and (2) delivering hands-on, guided training sessions at customer sites around the world. • Work is project-based, averaging roughly one week per month, and is ideal for an experienced professional who wants to stay engaged without a full-time commitment. • This is a non-converting contract engagement. Hours and travel are not guaranteed week-to-week; when a training trip is scheduled, you go — when it is not, there is no work. • The predictable portion of the role is demo and curriculum creation; the training delivery side fluctuates.
World’s leading Data Intelligence Platform supercharging over 500,000 GPUs across all data workloads
Role Description DDN is seeking a Client Director – Strategic AI Infrastructure to drive revenue growth in the U.S. West Coast region within our Artificial Intelligence business unit. This is a quota-carrying role responsible for developing strategic enterprise opportunities and expanding DDN’s presence within organizations building large-scale AI and high-performance computing environments. In this role, the Account Executive will operate as a Strategic AI Client Director, partnering with enterprise customers, research institutions, and cloud organizations to deliver high-performance data infrastructure solutions that support advanced AI workloads. The successful candidate will work closely with internal engineering teams, solutions architects, and channel partners to design and deliver complex AI infrastructure solutions supporting GPU computing, large-scale model training, and data-intensive workloads. - Act as a Strategic AI Client Director within the assigned West Coast territory, driving adoption of DDN’s AI infrastructure solutions across enterprise and research organizations. - Drive revenue growth and achieve or exceed assigned sales quota across the West Coast region. - Develop and execute strategic territory and account plans targeting organizations building large-scale AI and high-performance computing environments. - Identify and qualify new opportunities across industries including financial services, healthcare, telecommunications, technology, research institutions, and large enterprise organizations. - Build trusted relationships with senior technical leaders, AI infrastructure teams, and executive decision-makers within target accounts. - Partner with solutions architects and engineering teams to design and present AI infrastructure and high-performance data storage solutions. - Deliver compelling presentations, product demonstrations, and proposals showcasing the value of DDN’s AI-optimized storage platforms. - Manage the full enterprise sales cycle including pipeline development, forecasting, contract negotiation, and deal closure. - Develop strong relationships with channel partners, system integrators, OEM partners, and distributors to expand market reach. - Stay current on market trends related to AI infrastructure, GPU computing, large-scale model training, and high-performance data environments. - Represent DDN at industry events, conferences, and customer engagements to generate new opportunities and strengthen market presence. Qualifications - 8+ years of experience in enterprise technology sales, preferably within storage, compute, networking, or infrastructure platforms. - Demonstrated experience selling AI infrastructure, HPC solutions, cloud platforms, or enterprise data center technologies. - Experience managing complex enterprise sales cycles involving technical buyers and executive stakeholders. Requirements - Experience selling AI infrastructure, GPU clusters, or high-performance computing platforms. - Familiarity with the AI ecosystem including technologies from NVIDIA, Intel, AMD, Cerebras, Graphcore, or Google TPU. - Knowledge of enterprise storage technologies including SAN, NAS, object storage, and parallel file systems. - Experience working with channel partners, system integrators, and distributor ecosystems. - Familiarity with technologies such as Kubernetes, OpenStack, and large-scale AI data center environments. Benefits - At DDN, you will work at the forefront of AI infrastructure innovation, helping organizations solve some of the most demanding data challenges in the world. - Our teams collaborate across engineering, sales, and customer success to deliver cutting-edge technology that powers the next generation of AI breakthroughs.
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.
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 .
• Define product strategy and roadmap for GPU instances, clusters, and services • Manage the entire product lifecycle from planning to end-of-life • Develop and execute go-to-market strategies, messaging, and launch plans • Collaborate with ecosystem partners on roadmap and technical needs • Translate AI, HPC and graphics workload needs into specifications and performance goals • Oversee GPU infrastructure lifecycle components and implement data-driven decisions • Identify enhancements for proactive monitoring and support processes
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 own 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 complex 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. - Non-exempt employees receive 16 days of paid vacation time per full calendar year. - Exempt employees participate in Cisco’s flexible vacation time off program. - 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.
Vultr is on a mission to make high-performance cloud computing easy to use, affordable, and locally accessible.
• Lead the engineering team responsible for the day-to-day implementation, scaling, and operation of AI compute clusters. • Translate engineering roadmaps and technical requirements from the Director of AI Infrastructure into detailed project plans and execution milestones. • Drive delivery of cluster deployments, hardware bring-up, node configuration, and integration with orchestration and scheduling systems. • Ensure cluster reliability, uptime, and performance through monitoring, automation, and continuous operational improvements. • Oversee lifecycle operations for bare metal and GPU fleets, including provisioning, configuration management, firmware/driver updates, and hardware validation. • Manage incident response for GPU and cluster infrastructure, ensuring timely resolution and root-cause analysis. • Work closely with AI/ML, SRE, Networking, and Hardware Engineering teams to ensure cluster capabilities meet training and inference needs. • Coordinate with Product to confirm technical requirements, feature readiness, and delivery timelines. • Support integrations across networking, storage, scheduler, and resource orchestration components. • Improve tooling and automation for cluster provisioning, observability, configuration management, and large-scale fleet operations. • Contribute to the development and refinement of multi-tenant scheduling, workload management, and orchestration systems in partnership with senior technical staff. • Identify performance bottlenecks and propose engineering-level optimizations. • Coach and mentor engineers, fostering a high-performance, detail-oriented engineering culture. • Support career development, expectations, and performance management for team members. • Help refine engineering processes, including code reviews, testing standards, documentation, and operational runbooks.
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.
Role Description We are looking for an LLM Fine-Tuning 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. 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. Requirements - 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. Benefits - Competitive base salary commensurate with experience, plus benefits.
Vultr is on a mission to make high-performance cloud computing easy to use, affordable, and locally accessible.
• Strategic Account Ownership: Own and grow strategic customer relationships, acting as the primary point of contact for executive stakeholders and technical teams alike. • Drive AI Infrastructure Revenue: Accelerate the adoption of Vultr’s AI cloud infrastructure solutions by identifying opportunities and guiding customers through the sales cycle—from initial engagement through solution architecture and scale-up. • Customer-Centric Engagement: Understand customer priorities, technical requirements, and business goals to position Vultr’s value proposition effectively and deliver tailored solutions. • Trusted Advisor: Provide thought leadership on AI/ML trends, infrastructure needs, and optimization strategies to senior leaders within your accounts. Help customers navigate the AI landscape and make informed architectural decisions. • Collaborate for Success: Work cross-functionally with Product Management, Solutions Engineering, and Customer Success to ensure alignment on product capabilities, roadmap feedback, and long-term success. • Sales Process Excellence & Operational Hygiene: Document and maintain accurate sales activity data in CRM tools to support MRR forecast accuracy. • AI Ecosystem Engagement: Understand and leverage the AI partner ecosystem to enhance Vultr’s value proposition for Founders and C-suite executives.
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.
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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Cloud, Kubernetes, Linux, Python, PyTorch, Tensorflow
