Machine Learning Engineer
Build and own ML models, fine-tuning, evaluation harnesses, and routing for Kepler's AI agent harness in finance. Requires 5+ years production software experience and shipped ML systems focused on correctness, evals, and real-world reliability.
About the job
Responsibilities
- Own the models inside Kepler's AI research platform: select which model runs each task, decide when a fine-tuned model beats a frontier one, and manage the training, evaluation, and extraction systems.
- Fine-tune small models on high-volume extraction tasks (e.g., footnote tables in 10-Ks, IR decks) and demonstrate improvements in accuracy, cost, and latency.
- Build evaluation harnesses that score agent research runs end-to-end (ensuring every number traces and every citation resolves) and integrate into CI to catch regressions.
- Redesign model routing across workflows: use frontier models for hard reasoning, cheaper or fine-tuned models for high-volume extraction and verification, backed by evals.
- Systematically improve workflows that succeed 80% of the time by identifying and addressing the remaining 20% through better tools, tighter verification rules, different context, fine-tunes, or model changes.
- Own systems end-to-end, from extending the platform to new industries to leading new architecture as infrastructure scales.
- Ship production systems with a focus on correctness, handling failure modes, regressions, subtle bugs, and debugging before demos.
Requirements
- 5+ years building production software (no upper limit; compensation scales with experience).
- Shipped ML systems to production, including fine-tuning, agents, retrieval, and structured extraction; understand what breaks between a demo and a product.
- Treat evals as engineering: build measurement before the feature and only call something better when numbers confirm it.
- Strong general engineering fundamentals, regardless of path into ML (research, ML infra, or product).
- Comfortable moving between a fine-tuning run and orchestrator code in the same day; able to work in a codebase you didn't write.
- Care about what analysts do with what you ship, not just whether the code was clever.
- Prefer fixing issues over filing tickets; proactively communicate design flaws before PRs.
- Strong communicator who anticipates problems and supports teammates without being asked twice.
- Thrive in a fast-paced environment where plans change frequently but work still ships.
- Low ego, willing to handle unglamorous problems and roll up sleeves in a small team.
Nice-to-Haves
- Experience with Rust (backend is Rust, but not required; strong fundamentals in other languages suffice).
- Background in finance, high-stakes industries, or building systems at scale (e.g., Palantir, Meta).
Compensation and Benefits
- Competitive compensation scaling with experience.
- 100% covered top-of-the-line medical benefits.
- Direct mentorship from engineers who built Palantir's core systems, including weekly 1:1s, architectural reviews, and a clear path to technical leadership.
Skills
Machine Learning, Fine-Tuning, Model Evaluation, Agents, Retrieval, Structured Extraction, Rust, Python, AWS, Postgres, TypeScript, React
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