Applied Research - Chip Design

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

Chip design is a natural fit for agentic reinforcement learning: long-horizon work, specialized tools, expensive mistakes, hard constraints, and objective feedback from simulation, verification, timing, power, and area. You will build agents, environments, evaluations, and post-training systems for real semiconductor design and verification workflows. The work is grounded in an external design workflow, with access to relevant domain experts and toolchains.

It may span RTL generation and debugging, formal verification, simulation, physical design, optimization, and orchestration across EDA tools.

Responsibilities

• Applied Research at Prime
• We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities.
• Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.
• Representative early problems
• Build an interactive verification environment where an agent reads a specification, writes or repairs RTL, invokes simulation or formal tools, interprets failures, and iterates toward a verified result.
• Design rewards and evaluations that combine functional correctness with tool efficiency, robustness, and performance/power/area constraints without opening easy shortcuts.
• Train long-horizon agents to recover from design and tool failures, preserve useful state, and generalize across blocks, designs, and constraint sets.

What you will own

Interactive environments where agents use EDA tools to modify designs, debug failures, and plan multi-step work.

Verifiers and rewards grounded in tests, simulation, formal methods, design rules, and engineering outcomes.

Experiments on credit assignment when feedback arrives late and several tools contribute.

Evaluations for correctness, recovery, and generalization, with reusable methods for other simulator-grounded engineering domains.

Requirements

• We are looking first for depth in RL, agents, or post-training systems and the ability to become fluent in semiconductor workflows.
• Prior work in design automation, compilers, formal methods, or ML for systems is useful but not required.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
• Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.
• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.
• Evidence of exceptional work
• We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.