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Remote Jobs by Role, Salary, and Tech Stack

C

Senior ML Engineer - Kimchi (LLM Inference Optimization

CAST AI

LLM Engineer12 hours ago

Full Time Remote Senior

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.

View details: Senior ML Engineer - Kimchi (LLM Inference Optimization

Worldwide

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AS-ISG-TE-HLS GenAI / LLM

Zensar

At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

LLM Engineer14 hours ago

Full Time Remote Mid LevelTeam 10,001

Role Description At Zensar, we are at the forefront of AI and web development, and we are looking for a talented individual to join our team and contribute to our innovative projects. The AS-ISG-TE-HLS GenAI/LLM role is a key position, where you will have the opportunity to work with a diverse range of technologies and make a significant impact. Your expertise will be utilized to develop and enhance our AI-powered solutions, utilizing Azure OpenAI and LangChain frameworks. - Knowledge of Frontend technologies like REACT or NEXTJS is valued. Qualifications - Experience with AI and web development. - Familiarity with Azure OpenAI and LangChain frameworks. - Knowledge of Frontend technologies like REACT or NEXTJS is a plus. Requirements - Ability to work with a diverse range of technologies. - Strong problem-solving skills. - Ability to contribute to innovative projects. Benefits - Inclusive workplace culture. - Opportunities for growth and development. - Commitment to well-being and individual celebration.

View details: AS-ISG-TE-HLS GenAI / LLM

India

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C

Senior ML Engineer - Kimchi (LLM Inference Optimization)

CAST AI

LLM Engineer1 day ago

Full Time Remote Senior

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.

View details: Senior ML Engineer - Kimchi (LLM Inference Optimization)

Worldwide

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Senior NLP/LLM Engineer

Social Discovery Group

Top world’s largest social discovery company uniting 70+ brands with 500M+ users

LLM Engineer1 day ago

Full Time Remote SeniorTeam 1,001-5,000Since 20 yearsH1B No Sponsor

Company Site LinkedIn

• Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques (SFT, RLHF, Adapters, etc.) to enhance the capabilities of AI models. • Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess the performance, accuracy, and user satisfaction of AI bots, including offline and online metrics. • Optimizing models for inference: Improve the efficiency and speed of AI models during inference to ensure they meet the performance and scalability requirements for production environments. • Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions into the overall product pipeline.

Numpy Pandas Python PyTorch Scikit-Learn

View details: Senior NLP/LLM Engineer

Worldwide

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AI Automation Engineer, Generative AI

eCom Solutions Inc

Our brains @ your work

LLM Engineer3 days ago

Full Time Remote SeniorTeam 201-500H1B Sponsor

Company Site LinkedIn

• Build and integrate AI-powered applications, workflows, and automation systems • Develop LLM-based tools, AI agents, chatbots, and internal productivity solutions • Integrate OpenAI, Anthropic, Gemini, or other generative AI APIs into business systems • Design AI automation solutions for operations, sales, customer support, recruiting, and productivity use cases • Build and improve RAG systems, document search tools, and knowledge-based AI applications • Work with APIs, databases, cloud services, and third-party platforms • Optimize prompts, workflows, outputs, and AI interactions for accuracy and usability • Collaborate with engineering, product, and business teams to deliver scalable AI solutions • Troubleshoot and improve AI workflows, including handling edge cases, errors, and unreliable outputs • Help ensure AI solutions are practical, secure, and aligned with business needs

Cloud Python

View details: AI Automation Engineer, Generative AI

Albania

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AI Infrastructure TPM II

Astreya

IT services that put people at the center of your business

LLM Engineer3 days ago

Full Time Remote LeadTeam 1,001-5,000Since 2001H1B Sponsor

Company Site LinkedIn

• Lead large-scale AI infrastructure deployment programs across multiple sites, regions, or business units. • Drive end-to-end project execution for GPU clusters, AI compute environments, storage platforms, high-speed networks, and data center infrastructure. • Develop integrated project plans, implementation strategies, and operational readiness frameworks. • Manage cross-functional coordination between engineering, operations, supply chain, vendors, and executive stakeholders. • Identify and mitigate program risks, schedule impacts, technical dependencies, and operational constraints. • Lead infrastructure migration, expansion, upgrade, and modernization initiatives. • Drive governance reviews, project reporting, KPI tracking, and executive-level communications. • Coordinate infrastructure acceptance testing, deployment validation, and production readiness activities. • Mentor junior project managers and contribute to PMO process standardization and operational maturity. • Support vendor negotiations, technical evaluations, and infrastructure planning initiatives.

PMP

View details: AI Infrastructure TPM II

California

$73.0K - $100.8K / year

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AI Infrastructure DC Design Intern

Astreya

IT services that put people at the center of your business

LLM Engineer4 days ago

Internship Remote Entry LevelTeam 1,001-5,000Since 2001H1B Sponsor

Company Site LinkedIn

• Assist in creating and updating AutoCAD drawings, rack elevations, cabinet layouts, and structured cabling documentation. • Support data hall design documentation, asset tracking, and revision management activities. • Assist engineering teams with infrastructure inventory validation and basic capacity tracking. • Help maintain design standards, templates, and project documentation repositories. • Participate in engineering reviews, design walkthroughs, and quality assurance activities. • Coordinate with cross-functional teams to gather project inputs and update design records. • Support preparation of project reports, spreadsheets, diagrams, and technical documentation. • Learn data center infrastructure concepts including power, cooling, cabling, rack configurations, and AI cluster environments. • Follow established operational procedures, engineering standards, and safety requirements. • Assist with administrative and project coordination tasks related to infrastructure deployment activities.

Cloud

View details: AI Infrastructure DC Design Intern

California

$15 - $24 / hour

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Principal AI Researcher – Agentic Systems, AI Infrastructure

Trase

AI, Uncomplicated.

LLM Engineer4 days ago

Full Time Remote LeadTeam 11-50Since 2023H1B No Sponsor

Company Site LinkedIn

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction. • Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact. • Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure. • Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls. • Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis. • Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization. • Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows. • Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments. • Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership. • Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

Cloud Java Python

View details: Principal AI Researcher – Agentic Systems, AI Infrastructure

Virginia + 1 more

$250K - $300K / year

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Principal AI Researcher – Agentic Systems, AI Infrastructure

Red Cell Partners

Impact Through Innovation

LLM Engineer4 days ago

Full Time Remote LeadTeam 11-50H1B Sponsor

Company Site LinkedIn

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction. • Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact. • Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure. • Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls. • Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis. • Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization. • Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows. • Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments. • Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership. • Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

Cloud Java Python

View details: Principal AI Researcher – Agentic Systems, AI Infrastructure

Virginia + 1 more

$250K - $300K / year

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AI Infrastructure Datacenter Technical Project Manager II

Astreya

IT services that put people at the center of your business

LLM Engineer5 days ago

Full Time Remote LeadTeam 1,001-5,000Since 2001H1B Sponsor

Company Site LinkedIn

Role Description The AI Infrastructure Datacenter Technical Project Manager Level 2 serves as a senior project leader responsible for managing large-scale AI infrastructure programs, complex technical deployments, and cross-functional strategic initiatives. This role drives execution excellence across compute, GPU, storage, networking, and data center infrastructure domains while ensuring alignment with business and operational objectives. Key Responsibilities - Lead large-scale AI infrastructure deployment programs across multiple sites, regions, or business units. - Drive end-to-end project execution for GPU clusters, AI compute environments, storage platforms, high-speed networks, and data center infrastructure. - Develop integrated project plans, implementation strategies, and operational readiness frameworks. - Manage cross-functional coordination between engineering, operations, supply chain, vendors, and executive stakeholders. - Identify and mitigate program risks, schedule impacts, technical dependencies, and operational constraints. - Lead infrastructure migration, expansion, upgrade, and modernization initiatives. - Drive governance reviews, project reporting, KPI tracking, and executive-level communications. - Coordinate infrastructure acceptance testing, deployment validation, and production readiness activities. - Mentor junior project managers and contribute to PMO process standardization and operational maturity. - Support vendor negotiations, technical evaluations, and infrastructure planning initiatives. Scope & Complexity - Leads highly complex infrastructure programs with multiple concurrent workstreams. - Manages enterprise-scale AI infrastructure deployments and operational initiatives. - Influences program execution standards, governance models, and delivery methodologies. Qualifications - Advanced understanding of AI infrastructure technologies including GPU platforms, storage systems, networking, and data center operations. - 8+ years of technical project or program management experience within infrastructure environments. - Proven experience leading large-scale infrastructure deployment or transformation programs. - Strong risk management, executive communication, and stakeholder alignment skills. - Experience coordinating multi-vendor and cross-functional technical teams. - Ability to manage complex schedules, budgets, and operational dependencies. - Relevant certifications preferred (PMP, PgMP, ITIL, Agile, CCNA, etc.). Requirements - Salary Range: $72,960.00 - $100,800.00 USD (Salary) - Please note that the salary information provided herein is base pay only (gross); it does not include other forms of compensation which may or may not apply to this specific position, namely, performance-based bonuses, benefits-related payments, or other general incentives - none of which are guaranteed, may be subject to specific eligibility requirements, and are wholly within the discretion of Astreya to remit. - Further, the salary information noted above is a range that consists of a minimum and maximum rate of pay for this specific position. Where an applicant or employee is placed on this range will depend and be contingent on objective, documented work-related considerations like education, experience, certifications, licenses, preferred qualifications, among other factors. Benefits - Medical provided through UHC (PPO, HSA, Surest options) - Medical provided through Kaiser (HMO option only) for California employees only - Dental provided through UHC - Nationwide Vision provided by UHC - Flexible Spending Account for Health & Dependent Care - Pre-Tax Account for Commuter Benefit/Parking & Transit (location-specific) - Continuing Education and Professional Development via various integrated platforms, e.g. Udemy and Coursera - Corporate Wellness Program provided by Goomi Group - Employee Assistance Program - Wellness Days - 401k Plan - Basic and Supplemental Life Insurance - Short Term & Long Term Disability - Critical Illness, Critical Hospital, and Voluntary Accident Insurance - Tuition Reimbursement (available 6 months after start date, capped) - Paid Time Off (accrued and prorated, maximum of 120 hours annually) - Paid Holidays - Any other statutory leaves, paid time, or other ancillary benefits required under state and federal law

View details: AI Infrastructure Datacenter Technical Project Manager II

United States

$73.0K - $100.8K / year

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Senior ML Engineer - Kimchi (LLM Inference Optimization

AS-ISG-TE-HLS GenAI / LLM

Senior ML Engineer - Kimchi (LLM Inference Optimization)

Senior NLP/LLM Engineer

AI Automation Engineer, Generative AI

AI Infrastructure TPM II

AI Infrastructure DC Design Intern

Principal AI Researcher – Agentic Systems, AI Infrastructure

Principal AI Researcher – Agentic Systems, AI Infrastructure

AI Infrastructure Datacenter Technical Project Manager II

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Senior ML Engineer - Kimchi (LLM Inference Optimization

AS-ISG-TE-HLS GenAI / LLM

Senior ML Engineer - Kimchi (LLM Inference Optimization)

Senior NLP/LLM Engineer

AI Automation Engineer, Generative AI

AI Infrastructure TPM II

AI Infrastructure DC Design Intern

Principal AI Researcher – Agentic Systems, AI Infrastructure

Principal AI Researcher – Agentic Systems, AI Infrastructure

AI Infrastructure Datacenter Technical Project Manager II