Applied ML Engineer - Model R&D

Role overview Turn experimental machine learning work into dependable software that can run and scale in production. Most of the role focuses on applied engineering and deployment, with some collaboration on research; the work spans model pipelines, system quality, and a transition toward Rust based infrastructure.

Responsibilities - Refactor research prototypes into maintainable, deployable production systems. - Build and operate end to end ML pipelines, including data ingestion, feature engineering, model serving, monitoring, and reliability. - Establish and use team engineering practices such as task tracking, Git workflows, code review, and CI/CD. - Work with researchers to translate ideas into observable systems, and identify and address architectural weaknesses. - Develop logging, monitoring, and alerting so operational issues are detected promptly. - Help support the codebase as it moves toward Rust native infrastructure.

Requirements - Professional experience in a collaborative software engineering environment with shared code ownership, architecture decisions, and rigorous reviews. - A strong software building focus, with the ability to turn ambiguous or unfinished work into production ready systems. - Clear communication and collaboration skills, including the ability to give and receive direct technical feedback. - Comfort working autonomously in a fast moving environment with open ended problems. - Interest in AI and ML systems and willingness to learn from research focused colleagues.

Nice to have - Rust experience or readiness to learn and contribute to Rust codebases. - Familiarity with model training, inference pipelines, NLP, or text based signal extraction. - Exposure to MLOps tools such as experiment tracking, model versioning, feature stores, or inference serving. - Interest in financial markets.

Benefits and work setup Remote first work with expected overlap with U.S. business hours; the team also holds periodic in person sprints and retreats. The listing describes flexible hours and location, performance linked compensation, home office and learning budgets, health coverage, retirement matching, paid parental leave, and an open vacation policy.