[Hiring] Senior Machine Learning Engineer @FYT

Role Description

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

Qualifications

• 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"

Requirements

• 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

Benefits

• 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

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