Jobs at Prime Intellect

Lead applied AI research programs in New York, combining hands-on work in reinforcement learning, post-training, agents, evaluation, and deployment. Establish research agendas, personally drive high-stakes experiments, recruit and develop researchers, set technical standards, and translate successful partner programs into shared research or product capabilities. The role is externally facing and requires strong experimental judgment, technical depth, Python expertise, and experience leading applied research or research-engineering teams.

Build post-training and agent systems for public-sector and regulated environments. Develop private improvement loops, permissioned tool-using agents, high-assurance evaluations, and deployable model updates within strict data and infrastructure boundaries. Conduct experiments, inspect traces, establish meaningful baselines, attribute failures, and collaborate directly with customers and research partners. The role requires hands-on model-training or agent experimentation, strong Python skills, sound research judgment, and comfort solving ambiguous problems.

Build environments, agents, evaluations, reward systems, and post-training methods for quantitative research and systematic decision-making. Develop realistic market and portfolio simulations, train long-horizon research agents, assess out-of-sample robustness, and create production feedback loops with monitoring and failure attribution. Collaborate directly with customers and research partners while maintaining a substantive research agenda.

Build environments, evaluations, agent systems, and post-training methods for embodied robotics in collaboration with an external robotics team. Train and evaluate agents using reinforcement learning, imitation learning, world modeling, planning, simulation, and multimodal perception. Own long-horizon agents, sim-to-real evaluations, reward systems, and training loops using offline and online feedback while accounting for safety and hardware constraints.

Build post-training and evaluation systems for multimodal computer-use agents operating browsers, desktops, and cross-application workflows. Develop long-horizon environments, reward signals, benchmarks, recovery strategies, and secure sandboxes using real workflows and production traces. Run experiments, inspect failures, attribute performance gains, and deploy successful research. The role involves external collaboration with customers, users, and research partners, requiring strong Python skills and ownership of ambiguous research problems.

Own externally facing applied reinforcement learning programs from ambiguous customer problems through deployment. Responsibilities include defining environments, evaluations, baselines, and reward signals; running post-training and distillation experiments; building agents with tool use and long-horizon capabilities; diagnosing model, data, harness, inference, and infrastructure failures; measuring customer impact; and converting successful work into reusable research, open-source systems, and platform capabilities.

Build agentic reinforcement-learning systems for semiconductor design and verification workflows. Develop interactive EDA environments, verifiers, reward functions, evaluations, and post-training experiments. Agents will generate or debug RTL, invoke simulation and formal tools, interpret failures, and optimize for correctness, efficiency, power, performance, and area. The role involves externally facing research, experimentation, failure analysis, and collaboration with customers, users, and research partners.

Conduct applied research on reinforcement learning for long-horizon, tool-using, multi-agent systems. Develop agentic judges, reward and credit-assignment methods, environments, evaluations, and scalable experimental systems. Diagnose reward hacking, data leakage, and infrastructure artifacts through rigorous experimentation. Build reusable open-source systems and publish findings while collaborating with customers, application teams, research partners, and open-source users.

Build continual learning systems that convert agent trajectories, corrections, and failures into training signals and model updates. Develop and evaluate streaming reinforcement learning, post-training, distillation, memory, and adaptation methods. Create reproducible task-generation, training, evaluation, regression, and rollback pipelines for changing task distributions. Conduct field experiments with external collaborators, assess retention and transfer, and establish promotion gates and human oversight for continuously improving production agents.

Own the development of open-source reinforcement learning, evaluation, agent, and environment systems. Build researcher-facing platform features, stable APIs, reference implementations, and technical documentation. Collaborate with engineers, customers, research partners, and external contributors to turn experimental research into reliable products and community contributions. Conduct model-training and post-training experiments, inspect traces, establish meaningful baselines, attribute failures, and improve workflows for researchers building and deploying advanced AI systems.

Build and optimize infrastructure for frontier-scale reinforcement learning and distributed model training. Responsibilities include improving compute, memory, networking, scheduling, kernels, communication paths, runtimes, and parallel training systems. The role contributes to RL training architecture, asynchronous rollouts, post-training systems, open-source ML infrastructure, and GPU performance optimization while collaborating with research and infrastructure teams.

Train and improve large language models from architecture experiments through full-scale pretraining runs. Responsibilities include extending pretraining code, evaluating MoE and attention architectures, profiling throughput, improving fault tolerance, investigating training stability, designing reproducible ablations, and collaborating on data, evaluation, midtraining, and long-context experiments.

Build and optimize GPU inference systems powering reinforcement learning research at frontier scale. Responsibilities include extending vLLM integrations, improving long-horizon rollout throughput, optimizing multi-node MoE inference, reducing trainer-to-inference update overhead, evaluating low-precision inference, debugging numerical issues, and collaborating with researchers on inference-efficient model architectures.

Build scalable pipelines that collect, clean, deduplicate, filter, and evaluate web, code, and technical training data. Develop data-quality and contamination checks, test data mixtures and synthetic data, support long-context, reasoning, and coding capabilities, and run controlled experiments to measure model impact. Ensure large-scale processing is efficient, reproducible, recoverable, and versioned while collaborating closely with AI researchers.

Research Engineer or Scientist developing reinforcement learning algorithms and systems for frontier AI models. Responsibilities include researching policy optimization, asynchronous RL stability, scaling behavior, controlled experiments, evaluations, training recipes, and distributed RL frameworks. The role also involves extending prime-rl, improving numerical correctness and performance, and diagnosing unstable training runs and regressions.

Own the zero-to-one product marketing function for Prime Intellect’s open AI infrastructure platform. Define positioning and category narrative, conduct customer research, support sales-led opportunities, lead product launches, create sales enablement materials, develop customer proof, and establish repeatable product marketing processes. Translate complex technical capabilities into clear messaging for developers, AI teams, and business buyers while using customer insights to inform product strategy and packaging.

Own technical discovery, architecture design, workload benchmarking, proofs of concept, and production planning for AI infrastructure customers. Partner with ML engineers and executives to demonstrate performance, latency, throughput, utilization, and cost advantages. Build repeatable benchmark methodologies, reference architectures, POC frameworks, and adoption playbooks while feeding customer insights into product and research priorities. The role serves as Prime Intellect’s technical lead throughout the sales cycle and requires expertise in ML infrastructure, distributed systems, GPU computing, training, inference, and customer-facing technical engagements.

Own strategic acquisition and expansion of AI-native and enterprise accounts for GPU infrastructure and the Model Factory platform. Build relationships with technical and economic buyers, translate AI roadmaps into multi-year capacity commitments, construct and negotiate seven- and eight-figure agreements, and expand customers across compute, training, post-training, inference, and platform adoption. Partner with Solutions Architecture, Compute, Finance, and leadership on technical validation, pricing, supply, forecasting, and commercial strategy.

Own employee onboarding from interview scheduling through the first weeks of employment, including equipment, access, payroll, benefits, compliance, HRIS setup, and offboarding. Coordinate recruiting logistics, interviews, candidate travel, and Ashby updates. Improve people operations through process automation and AI tools, track deadlines, support team events, and help create a strong in-person employee experience.

Own 3–5 strategic technical partnerships with NVIDIA, hyperscalers, GPU clouds, and infrastructure providers. Manage joint plans, agreements, customer engagements, co-selling, technical integrations, enablement, marketplace motions, and co-marketing. Bridge partner engineering, product, sales, and executive teams while translating roadmaps and customer feedback into product strategy. Build partner pipeline, measure revenue and technical outcomes, and develop a scalable partner program.