Manager, Machine Learning Engineering

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Manager, Machine Learning Engineering based in United States. This role leads a Machine Learning Engineering team responsible for the platforms, frameworks, and infrastructure that power production machine learning at scale. You will combine people leadership with hands-on technical direction across real-time inference, streaming data, batch processing, and ML deployment. The position is designed for a player-coach who can develop engineers while shaping architecture, engineering standards, and operational practices. You will help enable Data Science and Analytics teams to securely develop, deploy, monitor, and operate sophisticated models. The role works across engineering, data, product, credit, and business teams to translate evolving requirements into scalable solutions. Strong emphasis is placed on reliability, observability, automation, security, and production excellence. This is an opportunity to influence both the technical direction of ML infrastructure and the growth of a high-performing engineering team.

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.

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