Senior machine learning engineer

Senior ML Engineer

Be the ML force multiplier at a funded stealth startup solving hard on-device AI problems.

ABOUT THE COMPANY

A stealth startup in the identity space building privacy-preserving, on-device authentication via voice biometrics. Backed by $2.4M in pre-seed funding with large enterprise clients already in the pipeline. The core technical challenge: compressing 5GB ML models to under 5MB without sacrificing accuracy. Small team, real traction, massive upside.

WHY THIS ROLE EXISTS

We need someone who brings a different gear — modern tooling, fast experimentation, and a bias toward shipping. You won't be inheriting a broken system; you'll be raising the ceiling on what this team can do. This is the kind of role where the right person becomes indispensable within 90 days.

WHAT YOU'LL OWN

• Drive high-velocity experimentation on voice embedding models — speaker verification, liveness detection, age estimation
• Own model compression end-to-end — quantization, pruning, distillation — to hit aggressive on-device size and latency constraints
• Build modern ML infrastructure — experiment tracking, fast eval loops, reproducible pipelines — so the team can iterate at a different pace
• Deploy optimized models to iOS and Android via CoreML, TFLite, or ONNX Runtime and own their performance in production
• Partner with the co-founder to define the ML roadmap and unblock enterprise client integrations

YOU ARE THE RIGHT FIT IF

• You've shipped production ML models and measure yourself by what's deployed, not what's in a notebook
• You run 10 experiments where others run 2 — fast iteration is your default, not a mode you shift into
• You live in modern tooling — Weights & Biases, HuggingFace, modal, or equivalent — and have strong opinions about what a good ML stack looks like
• 5+ years of applied ML engineering with deep PyTorch or JAX fluency across the full training loop
• Hands-on experience with model compression — you've hit real size and latency walls and engineered your way through them
• Startup-ready: you set your own direction, don't wait to be unblocked, and thrive when the brief is "figure it out"

NICE TO HAVE

• Background in audio/speech ML — speaker verification, voice activity detection, or audio embeddings
• Experience with on-device deployment via CoreML, TFLite, or ONNX
• Familiarity with privacy-preserving ML — federated learning, on-device inference, differential privacy
• Prior experience at an early-stage startup — you know the difference between building for scale and building to learn