Senior ML Engineer

Frame ambiguous problems mathematically. Turn a business requirement like "catch deepfakes in KYC" into a well-posed objective: the right positive class, the right operating point, the right validation protocol that does not leak, and a metric that survives class-prevalence shift.

Train models, end to end. Own the full lifecycle for one or more model families: problem framing, data strategy, architecture choice, training, evaluation, deployment, monitoring, and retraining. You are accountable for the model in production, not just the notebook.

Evaluate with rigor. Design evaluation that predicts production behavior. Confusion matrices, ROC and PR curves, TPR at a fixed low FPR, calibration error, and cross-distribution generalization. Know why AUC can lie about a model you operate at FPR = 1e-4.

Optimize for inference at scale. Quantize, prune, and re-architect models so they serve within latency, throughput, and cost budgets. Move workloads off GPU to quantized CPU inference where the numbers justify it, and prove the accuracy held with a production canary before, not after. Deploy and operate resilient production systems that run 24x7

Reproduce and improve on research. Read a paper, reproduce its results, strip it down to what actually matters for our constraints, and ship it. We value simplification that preserves accuracy. A smaller, cheaper model that matches a heavier one is treated as a genuine result here, not a compromise.

Lead. Mentor 3 to 6 junior engineers on math-driven problem solving, experiment design, and ML systems. Set the bar for source discipline and reproducibility. Review models, not just code.