Research Engineer - Inference

Prime Intellect

San Francisco, CA

Remote

Who this role is best for

Geared toward candidates comfortable with optimizing inference systems for large-scale AI research, with a preference for those who have experience in GPU inference systems and can work collaboratively in a mission-driven team based in San Francisco or remotely.

Best fit for

Candidates with experience in GPU inference optimization and a background in ML framework development
— “Experience building or optimizing GPU inference systems”
Individuals who have worked on low-precision inference and can evaluate its impact on training
— “Build on FP8 and evaluate lower precision”
Candidates who can independently validate and scale complex AI workflows with RL integration
— “Extend prime-rl’s vLLM integration to bring new model architectures into RL training”

Things to consider

The role involves handling large-scale model deployments, including 1–3T parameter models
— “scaling toward 1–3T parameter models on NVL72 clusters”
This position requires expertise in both inference systems and collaboration with model researchers
— “Work with model researchers on architecture choices that improve inference efficiency”

How to stand out

Showcase your ability to optimize concurrency and KV-cache reuse in long-horizon agents
— “Improve rollout throughput for long-horizon agents, tuning concurrency and KV-cache reuse”
Emphasize any contributions to inference engines like vLLM or Triton in your application
— “Contributions to vLLM or another inference engine”
Demonstrate your skill in profiling and improving end-to-end ML workloads
— “Ability to profile end-to-end workloads and turn measurements into verified performance improvements”
Include examples of debugging numerical or integration issues in ML systems
— “Experience debugging numerical or integration issues in ML systems”

Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

optimized inference systems
improved model rollout throughput
reduced overhead

Typical background

3+ years in machine learningexperience with large-scale models

Skills & requirements

Required

GPU Inference SystemsPyTorchPerformance OptimizationNumerical Debugging

Preferred

Vllm ContributionsCUDA Kernel Work

Stack & domain

GPU Inference SystemsPythonPyTorchVllmCUDATritonFP8Moe InferenceKv-cache ReuseSharding-aware TransferCommunication OverlapTrainer/sampler Mismatch ChecksProblem-solvingTeamworkAISuperintelligenceInference SystemsRL Research

About the role

Original posting from Prime Intellect via Ashby

OWN YOUR INTELLIGENCE
Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.
Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators, including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.
ROLE IMPACT
Build the inference systems that power Prime Intellect’s RL research, scaling toward 1–3T parameter models on NVL72 clusters. Your work will improve rollout throughput and make frequent model updates practical.
RESPONSIBILITIES
Extend prime-rl’s vLLM integration to bring new model architectures into RL training, with small-scale validation before larger runs.
Improve rollout throughput for long-horizon agents, tuning concurrency and KV-cache reuse as context lengths grow.
Optimize multi-node MoE inference, including expert parallelism and the tradeoffs of separating prefill from decode.
Reduce trainer-to-inference weight-update overhead through sharding-aware transfer and communication overlap, while keeping rollout workers on the intended policy version.
Build on FP8 and evaluate lower precision. Use trainer/sampler mismatch checks to catch numerical regressions and measure the effect on RL training.
Work with model researchers on architecture choices that improve inference efficiency, balancing model quality against execution cost on the target hardware.
REQUIREMENTS
Experience building or optimizing GPU inference systems, with an understanding of inference-engine internals.
Strong Python skills and experience with PyTorch or a comparable ML framework.
Ability to profile end-to-end workloads and turn measurements into verified performance improvements.
Experience debugging numerical or integration issues in ML systems.
Nice to have
Contributions to vLLM or another inference engine.
CUDA or Triton kernel work, particularly for low-precision inference.
BENEFITS AND PERKS
Cash Compensation Range of $150-350k, including equity incentives, aligning your success with the growth and impact of Prime Intellect.
Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.
Visa sponsorship and relocation assistance for international candidates.
Quarterly team off-sites, hackathons, conferences and learning opportunities.
Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.
If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.

Source: Prime Intellect careers (Ashby)