Own applied RL programs from ambiguous customer problem to deployed improvement loop.
Understand workflows, build environments and evaluations, run post-training experiments, and deploy results.
Work directly with customer researchers, engineers, and leaders, accountable for research result and practical impact.
Prime Intellect builds the open superintelligence stack, providing a full-stack platform for frontier-scale post-training of AI models. The company has raised $150M from top investors and is seeking builders who want to work at the intersection of frontier research, real infrastructure, and go-to-market.
Design and build challenging evaluations and executable environments for coding and long-horizon agents.
Develop scoring logic and verifiers to detect reward hacks and trace failures through model outputs and harness code.
Build and scale RL data pipelines for generating and curating challenging coding and agentic tasks.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models and open-source models trained for long-horizon tasks. The company has raised $150M from top investors and is a mission-driven team passionate about accelerating science and AI.
Build and post-train agents for scientific research, including literature search, code execution, and domain tool use.
Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses, with strong baselines and evaluation.
Design verifiers, reward signals, and reproducible experimental loops for scientific workflows, ensuring evidence-based improvements.
Prime Intellect builds the open superintelligence stack, providing infrastructure for frontier AI labs to train, evaluate, and deploy models at scale. The company has raised $150M from top investors and operators, and is building a culture focused on frontier research, real infrastructure, and go-to-market for a new category.
Build reinforcement learning and agent environments for real customer and Frontier lab use cases, including task specifications, scoring, and evaluation.
Develop benchmarks and evaluation harnesses to measure model and data quality across accuracy, robustness, safety, latency, and cost.
Run fine-tuning, adapter, and other model experiments to evaluate how data and methods influence model behavior, and deploy local or self-hosted models for evaluation and inference.
Appen has been a leader in AI training data for over 30 years, specializing in human-generated data to train, fine-tune, and evaluate models across generative AI, LLMs, computer vision, and speech recognition. They support model development through an AI-assisted data annotation platform and a global crowd of over 1 million contributors in more than 200 countries, fostering a culture of innovation, collaboration, and humility over ego.
Own the research roadmap for recursive self-improvement and ship what works into production.
Design agent strategies, exploration policies, and evaluations for autonomous LLM research systems.
Partner with applied scientists and build production-quality experiments end to end.
Sequen builds Recursive Ranking Intelligence (RRI), an autonomous engine where AI agents perform ML research in clients' clouds or on Sequen-hosted instances. It is a small, highly technical, early-stage team turning AI advances into production ranking systems.
Develop RL algorithms for reasoning, coding, and long-horizon tasks.
Design and run scaling experiments to improve training recipes across model sizes.
Extend RL training frameworks for asynchronous workflows and reliability.
Prime Intellect builds the open superintelligence stack, offering a platform that unifies compute, training, and deployment for frontier AI teams. With $150M in funding and a mission-driven culture, they focus on open-source AI research and real infrastructure.
Build quantitative-research environments, agents, and post-training methods for systematic decision-making.
Turn workflows into tasks, run experiments against real baselines, and deploy what works.
Work directly with customers and quantitative funds while maintaining a substantive research agenda.
Prime Intellect builds the open superintelligence stack for post-training AI, unifying compute, environments, evaluations, and deployment. They have raised $150M from top investors and are building a culture of ambitious, externally-facing applied research.
Own the path from emerging research artifact to excellent open-source and product experience.
Build core open-source systems and shape researcher-facing features in our training platform.
Work with external researchers and contributors to turn fast-moving abstractions into stable public interfaces.
Prime Intellect is building the open superintelligence stack, providing infrastructure and a full-stack platform for post-training frontier AI models. The company has raised $150M in funding from prominent investors and aims to make open-source AI research fully reproducible and accessible.
Build post-training and evaluation systems for multimodal agents operating browsers and desktops.
Turn real workflows into environments, rewards, and experiments that drive reliable agent improvement.
Work directly with customers and research partners while maintaining a substantive research agenda.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models. The company has raised $150M in funding and fosters a culture of ownership and collaboration for ambitious AI teams.
Build RL environments, agentic systems, LLM pipelines, and evaluation frameworks for real-world AI use cases.
Develop benchmarks and evaluation harnesses to assess model quality across accuracy, safety, latency, and cost.
Conduct fine-tuning and model experiments, deploy self-hosted models, and document reproducible methodologies.
The employer is an organization focused on applied AI research, building practical and reusable AI systems for real-world use cases. It values curiosity, accountability, innovation, collaboration, and continuous learning in a remote environment.
Building agents, environments, and post-training systems for real semiconductor design and verification workflows.
Designing rewards and evaluations that combine functional correctness with tool efficiency, robustness, and performance/power/area constraints.
Owning interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work.
Prime Intellect builds the open superintelligence stack, unifying compute, environments, evaluations, and deployment for frontier-scale AI post-training. The company has raised $150M in funding from top investors like Founders Fund and NVIDIA, fostering a culture of building at the intersection of research, infrastructure, and go-to-market.
Build inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters.
Optimize rollout throughput, multi-node MoE inference, and weight-update overhead for long-horizon agents.
Collaborate with model researchers on architecture choices balancing model quality and execution cost.
Prime Intellect is building the open superintelligence stack, unifying compute, environments, and deployment for frontier-scale AI post-training. Backed by $150M in funding, the team is mission-driven and hard-working, focused on accelerating open AI research.
Build simulation and real-world environments for training and evaluating embodied agents.
Train long-horizon robots with RL, imitation learning, and world models under hardware constraints.
Run post-training experiments to find what works and share reusable open-source systems.
Prime Intellect builds the open superintelligence stack, making frontier AI infrastructure available to ambitious AI teams. With $150M in funding from top investors, they are a well-funded startup focused on open-source AI and decentralized training.
Design, run, and analyse large-scale RL post-training experiments such as RLHF and RL from verifiable rewards.
Research and develop methods that improve out-of-distribution generalization, including reward design, task diversity, and regularization.
Build held-out evaluations to measure generalization and catch reward hacking, and turn successful research into production training recipes.
Cohere is a leading security-first enterprise AI company building cutting-edge foundation models and end-to-end products for businesses. We are a global team of researchers, engineers, and designers passionate about their craft, headquartered in Toronto with offices worldwide.
Design agent architectures for planning, reasoning, tool use, and memory integration.
Improve reliability on long-running tasks with failure recovery and evaluation systems.
Build loops for agents to improve with real use, balancing quality, latency, and cost.
Adaption builds AI systems that evolve in real-time, making them flexible and personalized. They are a global-first team focused on talent density and collaboration.
Develop agentic research systems to automate mechanistic model development for biochemical and physiological processes.
Design and train machine learning models for in-context learning and few-shot adaptation on molecular and tabular data.
Collaborate with chemists and biologists to drive drug discovery decisions using AI models.
PostEra is building an AI-first biotech, using its Proton AI platform to accelerate the discovery of new medicines for patients. The company has a minimalist organizational structure, celebrating individual achievement through proportional compensation and collaboration toward delivering cures.
Build the Learning Gym, a sandboxed environment for training and evaluating AI agents on real Niural workflows with ground-truth reward signals.
Engineer verifiers, run reinforcement learning loops, and establish honest baselines to prove agent improvements generalize.
Write first-author research papers and internal technical reports, and represent the work externally through preprints and talks.
Niural is a global payroll, Employer of Record (EOR), Agent of Record (AOR), and Contractor Management platform. It is a relentlessly ambitious team focused on building foundational internet infrastructure for the AI era.
Build and operate production ML systems for recommendations, ranking, and personalized action selection across Stripe's Growth Platform.
Improve contextual bandit and policy-learning approaches, including exploration, reward design, and adaptive user feedback.
Develop reliable data and feature pipelines, reusable tooling, and online experiments to drive measurable business outcomes.
Stripe is a financial infrastructure platform for businesses of all sizes, enabling payments and revenue growth. With millions of companies as customers, Stripe fosters a culture of passion, grit, and integrity while working to increase the GDP of the internet.
Build the RL post-training stack end-to-end: rollout ingestion, reward computation, policy updates, and returning updated weights to the network.
Invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections.
Ship the first decentralized post-trained models, building evals and taking the release from run to public artifact.
Pluralis Research is developing Protocol Learning, a decentralized approach to training and serving large models on consumer devices. We are a small, deeply technical team backed by Union Square Ventures, with a remote-first culture and team members spread globally.
Build and ship LLM-powered applications and AI agent workflows.
Design systems for reasoning, planning, memory, tool use, and multi-step execution.
Optimize AI systems for quality, latency, and cost, and establish production practices for observability and evaluation.
Gina's Tech Jobs is a technology recruiting firm that connects companies with engineering and IT professionals. They focus on remote and onsite placements, offering personalized recruitment services and career support.