Manager, Machine Learning Engineering

What You'll Do

Lead & Grow the Team

• Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
• Hire, source, interview, and close strong MLE talent.
• Establish clear expectations, provide regular feedback, and create development plans for direct reports.
• Coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully.
• Create opportunities for engineers to take on challenging projects and grow their technical leadership.

Own Engineering Delivery

• Set quarterly goals and ensure the team consistently delivers against them.
• Own prioritization across product roadmap work, run-the-business activities, and operational excellence.
• Balance team capacity across new development, maintenance, technical debt, and production support.
• Improve team productivity by reducing context switching and delegating effectively.
• Partner with engineers and technical leads to estimate and scope complex work.

Provide Technical Leadership

• Guide the development of platforms and frameworks that allow Data Scientists and Analysts to explore data, develop features, and train, test, deploy, and monitor ML models.
• Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.
• Drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment.
• Own and improve SLOs, on-call health, capacity planning, reliability, and incident response.
• Review technical designs and help drive architectural standards and technical debt reduction.

Partner Across the Organization

• Work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams.
• Translate business and technical needs into scalable ML platform solutions.
• Coordinate dependencies and delivery across multiple engineering and data teams.
• Help create structure and clarity in an environment where priorities and requirements can evolve.

What You'll Need

Management Experience

• 2+ years of directly managing engineers, including hiring, performance management, coaching, and career development.
• Experience managing a team through at least one full performance cycle.
• Demonstrated ability to coach engineers toward promotion and address underperformance effectively.
• Experience owning team goals, prioritization, estimation, and delivery.
• Experience with production on-call, incident response, and capacity planning.
• Willingness to be actively involved in sourcing, interviewing, and closing engineering talent.

Technical Experience

• 6+ years of backend software engineering experience in consumer-scale applications.
• At least 3 years of hands-on Python experience.
• Experience building and operating machine learning or causal inference systems in production.
• Earlier-career experience personally building and deploying ML models or ML infrastructure.
• Ability to participate in technical architecture and system-design discussions and provide technical direction without needing to be the primary coder.
• Strong understanding of software quality, security, reliability, testing, and production operations.

Technical Skills

We’re particularly interested in candidates with experience across:

• Languages: Python, SQL
• Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face
• Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker
• Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming
• Batch Processing: Airflow, Metaflow
• Databases: MySQL, PostgreSQL, Cassandra, Snowflake, Druid, and/or similar technologies
• APIs: REST, GraphQL, gRPC, Protocol Buffers
• Production Engineering: DevOps, SLOs, monitoring/observability, on-call, capacity planning, root-cause analysis
• ML/Analytics: Machine learning, causal inference, scalable algorithms