Machine Learning Engineering Manager (Power)

• Lead, coach, and support a high-performing squad of Machine Learning Engineers, fostering psychological safety, professional growth, and a culture of continuous delivery.

• Drive the end-to-end technical direction and operational excellence for ML infrastructure, automated training, model evaluation, and low-latency inference pipelines.

• Partner with Product Managers, Data Scientists, and Data Engineering leads to align roadmaps, scope technical initiatives, and deliver high-impact features predictably.

• Define and champion engineering best practices across MLOps, system observability, data quality monitoring, and automated testing to ensure high reliability.

• Provide architectural guidance and participate in technical design reviews, helping the team evaluate trade-offs between speed, scalability, and long-term maintainability.

• Champion technical hiring and interview calibration to attract, evaluate, and onboard diverse engineering talent.

 

What you'll need (Must-haves)

• Proven experience in technical leadership or engineering people management, guiding small-to-medium squads through complex software delivery cycles.

• Strong software engineering background with hands-on experience deploying and operating machine learning pipelines in production environments (e.g., Python, Docker, Kubernetes).

• Deep understanding of MLOps lifecycle tools, workflow orchestration, model serving, and cloud infrastructure (AWS or equivalent).

• Demonstrated ability to build strong cross-functional alignment between engineering, data science, and product management.

Nice-to-haves

• Experience designing or scaling streaming ML systems handling high-frequency time-series data or spatial-temporal models.

• Exposure to modern ML platforms, feature stores, and distributed compute frameworks.

• Familiarity with commodity, maritime, energy, or financial market domains.