Senior Machine Learning Engineer

Build Optimization applications. Design, build and operate applications built around advanced optimization models in production at scale. This includes API design, data ingestion, multiprocessing, observability, advanced algorithm development, performance benchmarking, and automated testing.

Build automated evaluation and test model behaviour. Design and operate the evaluation system that supports development and delivering features with confidence.

Specify precisely and direct AI coding agents. Convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria; decompose work into agent-sized tasks; and review agent output for correctness and maintainability. Contribute to teams AI-harness helping shape the way agents are leveraged.

Own quality, safety, and reliability in production. Manage deployments, monitor service availability, implement alerting and respond to customer issues before they even know they have a problem.

Deliver as a forward deployed engineer. Work closely with customers when the work calls for it. Understanding business requirements and product capabilities to help customers get to value faster and use learnings to influence product roadmap.

Collaborate across product, design, and engineering. Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly.

5+ years of related experience with a bachelor’s degree in computer science, software engineering, or related technical field. Advanced degrees or certifications (especially in Operations Research) are a bonus.

Software engineering fundamentals. Strong command of OOP, data structures, algorithms, system design, concurrency, parallelism, APIs, data modeling, and testing.

Experience with key Technologies: Experience with Python is strongly preferred; experience in Java, JavaScript, Kubernetes, or GraphQL is a bonus.

Forward deployed delivery and stakeholder communication. Comfort working directly with customers under incomplete information and fixed timelines, judgment about when a fast local solution is right and when to hold out for the durable one, and credibility communicating capability, limitation, and risk to non-engineers.