Machine Learning Engineer (Governance ML Platform)

About this role

A Little About Us EDB provides a data and AI platform that enables organizations to harness the full power of Postgres for transactional, analytical, and AI workloads across any cloud, anywhere. EDB empowers enterprises to control risk, manage costs and scale efficiently for a data and AI led world. Serving more than 1,500 customers globally and as the leading contributor to the vibrant and fast-growing PostgreSQL community, EDB supports major government organizations, financial services, media and information technology companies.

EDB’s data-driven solutions enable customers to modernize legacy systems and break data silos while leveraging enterprise-grade open source technologies. EDB delivers the confidence of up to 99.999% high availability with mission critical capabilities built in such as security, compliance controls, and observability. For more information, visit www. enterprisedb .com Candidate Note: This position is 100% remote EDB is building the next generation of AI agent infrastructure on top of Postgres including an Agent Hub, an Agent Memory layer, MCP, governance and trust controls, and enterprise-grade retrieval.

We're looking for an experienced Machine Learning (ML) Engineer to own the ML infrastructure spanning serving, evaluation, red-teaming, and continuous improvement for governance models. And turning Postgres into a first-class platform for AI agents. What your impact will be: 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.