Serving & evaluations: low-latency serving, eval pipelines, model registry.
Research-to-production: <50ms inference via quantization/distillation, drift monitoring, shadow deployment, telemetry-to-training pipelines.
Red-team harness: attack orchestration, scorecards, regression suites, CI gating.
Self-improvement loop: continuous retraining, dataset curation from the audit trail, gated auto-rollout.
What you will bring
4–7 years of professional machine learning software engineering experience, with hands-on work on LLM-based or agentic systems.
Deep production ML serving experience (low-latency inference, quantization/distillation).
Evaluation pipeline and model registry experience.
Red-team/adversarial testing harness experience (CI-gated regression suites).
Drift monitoring/shadow deployment experience.
Fluency with SQL and PostgreSQL.
Security first mindset and experience in audited environments.
What will give you an edge
Experience building auto-retraining loops from production telemetry/audit data.
Familiarity with the Model Context Protocol (MCP), tool registries, and agent orchestration patterns.
Postgres extension development in C or Rust, or contributions to the Postgres ecosystem.
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