Introducing Moonlake, AI for creating real-time interactive content
Mission: As an applied AI Research Engineer: Code agents (post training + systems)
Scope of Work:
- Agentic systems design: Tool catalogs, function calling, program synthesis/repair loops, ReAct/Reflexion/ToT/LangGraph-style control, self-verification, sandboxed execution.
- Evaluation mindset: Build task suites for multi-step coding Full-stack LLM engineer: Prompt libraries, routing, retrieval, KV-cache management, streaming, telemetry.
- Security & isolation: Docker/firejail, network egress controls, secrets hygiene, dependency pinning, supply-chain sanity.
- Strong post-training: Supervised fine-tuning + preference/trace RL (DPO/RLAIF/RLHF), dataset curation, reward shaping, safety filters.
Tech signals:
- Has shipped agents that pass real repos' test suites end-to-end.
- Has published papers in agentic systems/ code gen Contributions to agent frameworks or OSS evals (LangGraph/AutoGen/Guidance/LEAP, SWE-bench variants, EvalPlus).
- Built datasets from execution traces; can show improvements from data > params.
We are committed to being an on-site, in-person team currently based in San Mateo
embedding-vc