Senior Machine Learning Engineer (LLMs) at Albi – Chicago | Jobyssey

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

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