Manager of Machine Learning Engineering

Join Tala as the Manager of Machine Learning Engineering, where you will lead the ML Platform team and manage a group of Machine Learning Engineers. This player-coach role involves developing and growing the team while providing technical leadership in architecture, engineering practices, reliability, and production systems. You will focus on real-time machine learning inference, streaming data systems, and the platforms that support batch model development and deployment.

• Manage and develop a team of Machine Learning Engineers, providing technical leadership and guidance in architecture and engineering practices.
• Drive the development of platforms and frameworks that enable Data Scientists to train, deploy, monitor, and operate machine learning models at scale.
• Collaborate with cross-functional teams to translate business and technical needs into scalable ML platform solutions, coordinating dependencies and delivery.

• Experience owning team goals, prioritization, estimation, and delivery
• 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
• Willingness to be actively involved in sourcing, interviewing, and closing engineering talent
• Experience with production on-call, incident response, and capacity planning
• Strong understanding of software quality, security, reliability, testing, and production operations
• Experience building and operating machine learning or causal inference systems in production
• Earlier-career experience personally building and deploying ML models or ML infrastructure
• At least 3 years of hands-on Python experience
• Ability to participate in technical architecture and system-design discussions and provide technical direction without needing to be the primary coder
• 6+ years of backend software engineering experience in consumer-scale applications
• Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker
• Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming
• Languages: Python, SQL
• ML/Analytics: Machine learning, causal inference, scalable algorithms
• APIs: REST, GraphQL, gRPC, Protocol Buffers
• Production Engineering: DevOps, SLOs, monitoring/observability, on-call, capacity planning, root-cause analysis
• Batch Processing: Airflow, Metaflow
• Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face
• Databases: MySQL, PostgreSQL, Cassandra, Snowflake, Druid, and/or similar technologies
• We’re particularly interested in candidates with experience across: