Applied ML Engineer

Sentient is hiring an Applied ML Engineer to build systems at the intersection of ML research and production software: reproducing/evaluating research methods, designing evaluation datasets and scoring methods, working with model weights/logits/inference infra, and turning research workflows into product experiences. Core focus is investigating how model-verification methods behave under fine-tuning, merging, quantization, distillation and deliberate evasion.

• Reproduce and evaluate research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring methods and experiment harnesses
• Build/extend evaluation infrastructure (runners, judges, persistence, orchestration, reporting)
• Investigate model-verification behavior under fine-tuning/merging/quantization/evasion
• Ship production-quality systems with APIs, background jobs, observability and docs

• Strong Python with hands-on PyTorch and Hugging Face Transformers
• Understanding of ML evaluation (dataset design, baselines, metrics, calibration, reproducibility)
• Ability to implement research methods from first principles
• Experience building production software beyond notebooks
• Comfort across backend and frontend (React/TypeScript)

Sourced via automated job-market scan on 2026-10-01; description summarized from the employer's own posting.