Top Tech Jobs & Startup Jobs

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 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.