Senior Machine Learning Engineer (LLMs)

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