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Remote role increases applicant pool but ML research specialization and Tier-2 brand limit broad competition.
Specialized ML research, behavioral modeling, and edge inference require domain-specific experience, reducing cross-industry fit.
Strict production ML requirements (PyTorch/Rust, edge/behavioural modeling) significantly narrow candidate eligibility.
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Develop and deploy lightweight ML models for adaptive platform behavior based on behavioral data and telemetry.
Build end-to-end ML pipelines including training, evaluation, and production packaging for classifiers and predictive models.
Optimize model inference for low latency and cost using methods like quantization, distillation, batching, and caching.
Proven experience shipping ML models into production with full ownership.
Strong programming skills in Python and/or Rust and experience with PyTorch or TensorFlow.
Background in anomaly detection, sequence modeling, or behavioral modeling.
Experience with edge, embedded, or low-latency inference environments.
Operates effectively at the intersection of research, product, and systems engineering with scientific rigor.
Experienced in transforming complex behavioral data into actionable and deployable ML systems.
Skilled in optimizing ML models for production environments with priorities on scalability and inference efficiency.