Machine Learning Engineer (Governance ML Platform)

Accountabilities: Own the machine learning infrastructure supporting governance models, including model serving, evaluation, monitoring, red-teaming, and continuous improvement. Build and maintain low-latency model-serving systems and evaluation pipelines designed for production workloads. Develop and manage model registries and supporting infrastructure for reliable model lifecycle management. Translate research and experimental models into production systems, including optimization techniques such as quantization and distillation to achieve low-latency inference. Build drift-monitoring and shadow-deployment capabilities to identify changes in model behavior and validate new versions safely. Develop telemetry-to-training pipelines that turn production signals and audit data into actionable inputs for model improvement. Design and maintain red-team testing infrastructure, including attack orchestration, scorecards, regression suites, and CI-based quality gates. Establish continuous self-improvement workflows covering dataset curation, retraining, validation, and controlled model rollout. Implement appropriate safeguards and deployment controls to support secure and auditable machine learning operations. Collaborate across engineering and AI-focused teams to advance infrastructure for AI agents, governance, trust, and retrieval capabilities. Contribute to a high-quality engineering culture through documentation, testing, monitoring, and continuous improvement. Requirements 4–7 years of professional experience in machine learning software engineering, with hands-on experience building LLM-based or agentic systems. Strong production experience with ML model serving, particularly low-latency inference, quantization, and model distillation. Proven experience building evaluation pipelines and working with model registries. Experience designing red-team or adversarial testing frameworks, including regression testing and CI-gated workflows. Practical experience with model drift monitoring and shadow deployment strategies. Strong SQL skills and solid working knowledge of PostgreSQL. Security-first mindset and experience developing or operating systems in audited, compliance-sensitive, or security-conscious environments. Strong software engineering and problem-solving skills, with the ability to take ML systems from experimentation through reliable production deployment. Ability to design scalable infrastructure and establish robust processes for model evaluation, monitoring, and continuous improvement. Strong communication and collaboration skills within distributed, cross-functional engineering environments. Experience building automated retraining loops using production telemetry or audit data is a plus. Familiarity with Model Context Protocol (MCP), tool registries, and AI agent orchestration patterns is an advantage. Experience developing PostgreSQL extensions in C or Rust, or contributing to the PostgreSQL ecosystem, is a plus. Benefits 100% remote work within the EMEA region. Opportunity to work on emerging AI agent infrastructure and machine learning governance technologies. Hands-on ownership of production ML infrastructure spanning serving, evaluation, security testing, and continuous improvement. Opportunity to work at the intersection of machine learning, AI agents, PostgreSQL, security, and enterprise technology. Access to health and wellness resources, including CuraLinc. Wellness Fridays available through December 2026. Additional region-specific benefits and perks depending on location. Remote-first environment supporting collaboration across an international engineering organization. Opportunity to contribute to the development of secure, reliable, and enterprise-ready AI technologies.