Manager, Machine Learning Engineering

Accountabilities:

• Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
• Recruit, source, interview, and close high-quality Machine Learning Engineering talent.
• Establish clear expectations, provide regular feedback, and create development plans for direct reports.
• Coach engineers toward technical growth and career progression while addressing performance gaps thoughtfully and directly.
• Create opportunities for team members to take on challenging initiatives and expand their technical leadership.
• Set quarterly objectives and ensure the team consistently delivers against agreed goals.
• Own prioritization across product roadmap initiatives, operational responsibilities, maintenance, technical debt, and production support.
• Balance team capacity across new development, platform maintenance, reliability, and engineering improvement initiatives.
• Improve team productivity through effective delegation, prioritization, and reduction of unnecessary context switching.
• Partner with engineers and technical leads to estimate, scope, and plan complex technical work.
• Guide platforms and frameworks that enable Data Scientists and Analysts to explore data, develop features, and train, test, deploy, and monitor machine learning models.
• Provide technical leadership across ML infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.
• Establish strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment.
• Own and improve service-level objectives, on-call health, capacity planning, reliability, and incident response practices.
• Review technical designs and contribute to architectural standards and technical debt reduction.
• Collaborate closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams.
• Translate business and technical requirements into scalable ML platform solutions.
• Coordinate dependencies and delivery across multiple engineering and data teams.
• Provide structure and clarity when priorities, requirements, and technical challenges evolve.
• Promote secure, reliable, and maintainable production engineering practices across the ML platform.

Requirements:

• 2+ years of direct engineering management experience, including hiring, performance management, coaching, and career development.
• Experience managing engineers through at least one complete performance cycle.
• Demonstrated ability to coach engineers toward promotion and address performance concerns effectively.
• Experience owning team goals, prioritization, estimation, and delivery.
• Experience with production on-call operations, incident response, and capacity planning.
• Willingness to remain actively involved in sourcing, interviewing, and hiring engineering talent.
• 6+ years of backend software engineering experience building consumer-scale applications.
• At least 3 years of hands-on Python development experience.
• Experience building and operating machine learning or causal inference systems in production.
• Earlier-career experience personally building and deploying machine learning models or ML infrastructure.
• Ability to participate in technical architecture and system-design discussions and provide technical direction without necessarily serving as the primary coder.
• Strong understanding of software quality, security, reliability, testing, and production operations.
• Strong proficiency with Python and SQL.
• Experience with machine learning technologies such as Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face.
• Experience with cloud and infrastructure technologies such as AWS, GCP, Azure, Kubernetes, and Docker.
• Familiarity with streaming technologies including Kafka, Kinesis, Beam, Flink, or Spark Streaming.
• Experience with batch-processing technologies such as Airflow or Metaflow.
• Experience with databases such as MySQL, PostgreSQL, Cassandra, Snowflake, Druid, or comparable technologies.
• Familiarity with APIs and communication technologies such as REST, GraphQL, gRPC, and Protocol Buffers.
• Strong production engineering knowledge covering DevOps, SLOs, monitoring and observability, on-call practices, capacity planning, and root-cause analysis.
• Knowledge of machine learning, causal inference, and scalable algorithm development.
• Strong communication and collaboration skills, with the ability to work effectively across technical and business functions.

Benefits:

• Annual salary range of $170,000–$210,000 USD.
• Remote-first work environment with distributed teams.
• Opportunity to lead and develop a team of Machine Learning Engineers.
• Opportunity to influence ML platform architecture and production engineering practices.
• Work involving machine learning infrastructure, real-time inference, streaming systems, and scalable data platforms.
• Exposure to cross-functional work spanning data science, data engineering, product, credit, and business development.
• Inclusive and diverse global working environment.
• Opportunities to contribute to technology designed to expand access to financial tools and services.
• A culture that values innovation, collaboration, and diverse perspectives.