Senior Machine Learning Engineer (LLMs)

About the role

A company building deeply integrated LLMs into a real product used daily by restoration companies is looking for a Senior Machine Learning Engineer. This is not a prompt-engineering role: you will design, train and ship domain-specific language models that automate real workflows and generate revenue. The role sits in Chicago.

Key responsibilities

• Own end-to-end LLM systems: architecture, training, evaluation 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
• Work closely with product and engineering teams 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, refactoring, testing and experiments

Required profile

• 5+ years of hands-on ML/AI engineering experience
• Proven experience training or substantially contributing to training LLMs, beyond API calls
• Ability to explain complex systems simply to non-technical stakeholders and dive deep with experts
• Track record of owning projects end-to-end and mentoring other engineers
• Self-taught in at least one major skill, enjoys deep work and dislikes pointless meetings

Required skills

• Transformer architectures, attention and training dynamics
• Python, PyTorch or JAX
• Large-scale data pipelines and experiment tracking
• Hands-on fine-tuning (LoRA, instruction/SFT, RLHF or similar)
• Nice to have: distributed training (FSDP, DeepSpeed, Megatron), inference optimization (quantization, speculative decoding, vLLM, Triton), production SaaS LLM features, open-source contributions, Microsoft Foundry experience

What we offer

• Competitive salary based on experience and location
• PTO, medical, dental and vision coverage, 401(k) plan
• High ownership and autonomy, direct collaboration with a small team
• Environment that values deep work, regular team events and off-sites
• Equipment and learning budget