Summary
What you’ll impact
The Machine Learning Engineer, Applied AI at the organization will design, build, and ship end-to-end AI decision systems that operate in regulated institutional contexts. The role involves creating composite AI pipelines, developing document-understanding models, designing evaluation frameworks, and collaborating directly with domain experts to deliver production-ready solutions.
Responsibilities
What you'll do
- Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML.
- Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them.
- Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them.
- Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off.
- Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops.
- Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well‑posed ML problems and production deployments.
- Own AI systems end‑to‑end. There is no handoff: the person who trains the model owns its behavior in production.
- Work at the research frontier with production stakes, applying LLMs, RL fine‑tuning, and agentic systems where the output is a decision an institution acts on.
- Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done.
- Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark.
- Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.
Requirements
What you’ll bring
- You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong.
- And you live at the bleeding edge of modern AI, with hard‑won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine‑tuning, tool use, and reasoning.
- That combination is the job: you know when a fine‑tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model.
- You treat frontier models as components to be measured, pushed, and engineered — never as magic.
- Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once.