Senior Machine Learning Engineer (LLMs)

Overview 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. Responsibilities 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 What We're Looking For “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 Qualifications 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 Compensation and 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 #J-18808-Ljbffr