Machine Learning Engineer

Machine Learning Engineer (All Levels) — Stealth-Mode AI Startup📍 Palo Alto, CA · Hybrid · Full-time · 1–10 yrsAbout usWe're a well-funded, stealth-mode startup backed by top-tier investors, building at the frontier of AI and the life sciences. We're a small founding team solving problems that genuinely matter — and we're looking for ML engineers who want their work to ship, not sit in a research backlog. You'd be one of our earliest engineering hires, with real ownership from day one.What you'll doTrain, fine-tune, and evaluate large models — SFT, DPO/RLHF, LoRA/PEFT — and own the full loop from data to deployed modelBuild and optimize serving and inference infrastructure for production-grade latency and throughputDesign evaluation frameworks and benchmarks that hold our models to a real-world bar, not just offline metricsPartner directly with founders and domain experts to turn hard scientific problems into ML systemsMove fast in a 0→1 environment where you set patterns rather than inherit themWhat we're looking forHands-on ML engineering experience — you've trained and shipped models, not just called APIsStrong Python and deep-learning framework fluency (PyTorch or equivalent)Real exposure to model training/fine-tuning and the production path (serving, optimization, eval)A builder's instinct: comfortable with ambiguity, biased toward shipping, eager to own outcomes end-to-endLevels & experienceWe're hiring across the full range — 1 to 10 years:Junior / Mid: strong fundamentals, some production ML, and the drive to grow fast with a founding teamSenior / Lead: deep training + serving expertise, with the judgment to set technical direction and raise the bar for those around youNice to haveExperience with life sciences, biology, or scientific/research dataInference optimization (TensorRT, Triton, quantization) or large-scale distributed trainingBackground building eval/benchmark infrastructure from scratchWhy nowFounding-team equity, frontier compute, and a problem space where the work is hard and meaningful. Hybrid in Palo Alto — we value the energy of building in the same room.