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