We’re building deeply integrated LLMs into a real product used daily by restoration companies running thousands of jobs. This is not a “prompt engineer” role. You’ll design, train, and ship domain-specific language models that automate real workflows and move real revenue.
You will:
- Own end‑to‑end LLM systems: architecture, training, evals, and iteration
- Fine‑tune and extend existing models (LoRA, instruction tuning, RLHF)
- Build and maintain data pipelines from product databases, documents, APIs, and logs
- Ship reliable, monitored, production models with clear guardrails
- Collaborate closely with product and engineering to turn messy real‑world problems into working systems
- Build and coordinate the AI engineering team
- Use Claude Code as a core tool for development, refactors, tests, and experiments
This is for you if:
- “How does this actually work under the hood?” is your default question
- You’re fine sitting with a hard problem for days and reading papers on weekends to figure it out
- If there’s something interesting to learn or solve, it doesn’t matter if it’s Saturday or 1 a.m., you’re in
- You build side projects nobody asked for and write cleaner code than anyone requires
- You’re quietly competitive, self‑taught in at least one major skill, and think in systems
- You’re slightly allergic to meetings without a clear purpose or owner
Requirements
- 5+ years of real world experience in ML / AI engineering
- Proven experience training or substantially contributing to training LLMs (not just calling APIs)
- Deep understanding of transformers, attention, and training dynamics
- Strong Python plus PyTorch or JAX
- Experience with large‑scale data pipelines and experiment tracking
- Hands‑on fine‑tuning (LoRA, instruction / SFT, RLHF or similar)
- Comfortable using Claude Code as part of your daily workflow
- Able to explain complex systems simply to non‑technical stakeholders and go deep with experts
- Track record of owning projects end‑to‑end and mentoring other engineers
Nice to have:
- Distributed training (FSDP, DeepSpeed, Megatron, etc.)
- Inference optimization (quantization, speculative decoding, vLLM, Triton)
- Experience shipping LLM features in production SaaS
- Open‑source contributions or published work or patents in ML / NLP
- Microsoft Foundry experience
Benefits
- Competitive salary (based on experience and location)
- Generous PTO
- Medical, dental, and vision coverage
- 401(k) plan
- High ownership and autonomy over your work
- Direct collaboration with a small team of smart, kind, motivated engineers
- An environment that values deep work, clear thinking, and real impact
- Regular team events and off‑sites
- Equipment and learning budget to help you do your best work and keep up with the frontier
