N
Normal Computing
AI Research Engineer
Skills
PythonPyTorchMachine LearningAI
2 months ago
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Important information
Contract type:
Permanent
Salary:
$200,000 – $400,000 / year
Location:
New York, NY, USA
Work mode:
Onsite
Published on:
9 August 2026
What they need
- We’re hiring an AI Research Engineer to push the frontier of agentic LLMs and reinforcement learning for our agentic code generation tool
- You’ll design and run experiments, build agents, curate datasets from complex technical documents (e.g. , chip specifications), and create rigorous evaluations
- You’ll write production‑quality research code and work closely with engineering to ship improvements to customers
- Leadership not required—impact through research and building is
- Design and implement multi‑agent and RL approaches for agentic code generation and tool‑use
- Build research prototypes that integrate with our agentic code generation tool; collaborate to productionize wins
- Create evaluation suites: task specs, pass/fail checkers, coverage, cost/latency dashboards
- Acquire and curate datasets from PDFs/logs/tables; generate synthetic data where appropriate; maintain data cards and licensing
- Analyze experiments with disciplined ablations; document results and decisions
- Stay current on LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis- Strong Python and ML framework experience (PyTorch preferred; JAX/HF a plus)
- PhD in CS/AI/ML (or equivalent research experience) with publications ideally in multi‑agent RL, agentic AI, or RL for language/code
- Demonstrated ability to turn research into working systems; reproducibility mindset (tests, seeds, configs, logging)
- Clear communicator who partners well with engineers
- Experience designing eval harnesses and success metrics for sequential/agentic tasks
- Comfortable with data acquisition/curation from documents/logs; good instincts about data quality and licenses
- Research on program synthesis/codegen, constrained decoding, or execution‑based rewards
- Experience with offline RL from tool traces or human corrections
- Open‑source contributions (e.g., CleanRL, RLlib, AutoGen, LangGraph, CrewAI, Transformers)
- Familiarity with semiconductor/chip domains or other complex technical specs
- Track record of shipping research to production and measuring impact
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