Staff Machine Learning Engineer

• Own technical direction for high-impact ML initiatives end to end, from problem framing and research exploration through production deployment and measurement, across document understanding, field extraction, and model-serving infrastructure
• Stay hands-on: prototype, fine-tune, and ship models yourself; write production code alongside the engineers you mentor
• Lead our LLM strategy, including fine-tuning open-weight foundation models, designing evaluation harnesses, and making principled build-vs-buy decisions between self-hosted and frontier API models across accuracy, cost, latency, and privacy constraints
• Bring a research-oriented mindset to production problems: read and translate current literature into practice, design rigorous experiments and ablations, and know when a published technique will or won't transfer to our data distribution
• Design the data flywheel, including labeling pipelines, human-in-the-loop feedback, and drift monitoring, so our models improve continuously from production signals
• Set engineering standards for model evaluation, calibration, reproducibility, and responsible deployment; be the person others seek out for design reviews on anything ML-shaped
• Mentor and multiply: grow senior and mid-level ML engineers through code review, pairing, and technical guidance; influence roadmaps in partnership with product and platform leadership

We’d love to chat if you have:

• Requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years of experience; or equivalent experience
• 8+ years in software/ML engineering with a demonstrated track record at Staff level or equivalent scope, leading technically while remaining deeply hands-on with the modern ML/AI stack (PyTorch, distributed training, experiment tracking, feature and vector stores)
• Direct experience fine-tuning LLMs, including supervised fine-tuning, LoRA/QLoRA, preference optimization (DPO/RLHF), and structured-output training, and taking fine-tuned models to production at scale
• Production experience in at least one of: recommendation systems, personalization, or search/ranking; you have built systems that learn from user behavior at scale, handled feedback loops and position bias, and shipped models where offline metrics had to survive contact with online reality
• Strong data reasoning fundamentals: sampling and class imbalance, evaluation design and metric selection, confidence calibration, leakage detection, distribution shift, and the statistical judgment to know when an A/B result or benchmark number is real versus noise
• Research-oriented mindset with shipping discipline: you follow the literature, form hypotheses, and run disciplined experiments, but you measure success by what reaches production and moves business metrics