Manager, Machine Learning Engineering

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead the Machine Learning Platform team at Tala.

• Oversee and nurture a team of 4–6 Machine Learning Engineers ranging from mid to senior levels.

• Recruit, source, interview, and secure top-tier MLE talent.

• Set clear expectations, deliver feedback, and formulate development plans.

• Mentor engineers to foster growth and advancement while addressing any performance issues.

• Establish quarterly objectives and guarantee consistent results.

• Take charge of prioritization across product roadmap initiatives, operational activities, and excellence in operations.

• Manage capacity between new development, maintenance, technical debt, and production support.

• Enhance productivity by minimizing context switching and delegating tasks effectively.

• Collaborate with engineers and technical leads to estimate and define the scope of complex projects.

• Steer the development of platforms and frameworks for data exploration, feature creation, and ML model training, testing, deployment, and monitoring.

• Provide technical guidance across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.

• Promote practices in testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment.

• Manage and enhance SLOs, on-call health, capacity planning, reliability, and incident response.

• Evaluate technical designs and enforce architectural standards while reducing technical debt.

• Work in partnership with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams.

• Convert business and technical requirements into scalable ML platform solutions.

• Coordinate dependencies and ensure timely delivery across engineering and data teams.

⛳️ Requirements

• Minimum of 2 years in direct management of engineers, including hiring, performance assessment, coaching, and career growth.

• Proven experience managing a team through at least one complete performance cycle.

• Demonstrated competency in coaching engineers towards promotion and effectively addressing underperformance.

• Experience in owning team objectives, task prioritization, estimation, and delivering results.

• Familiarity with production on-call responsibilities, incident response, and capacity planning.

• Eagerness to engage actively in sourcing, interviewing, and hiring engineering talent.

• At least 6 years of experience in backend software engineering for consumer-scale applications.

• A minimum of 3 years of practical experience with Python.

• Experience in building and managing machine learning or causal inference systems in production environments.

• Earlier career experience in personally constructing and deploying ML models or ML infrastructure.

• Ability to partake in technical architecture and system design discussions, providing technical guidance without being the primary coder.

• Strong grasp of software quality, security, reliability, testing, and production operations.

• Experience with Python and SQL.

• Familiarity with machine learning technologies such as Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, and Hugging Face.

• Experience with cloud services including AWS, GCP, Azure, Kubernetes, and Docker.

• Familiarity with streaming technologies such as Kafka, Kinesis, Beam, Flink, and Spark Streaming.

• Experience with batch processing technologies like Airflow and Metaflow.

• Knowledge of databases including MySQL, PostgreSQL, Cassandra, Snowflake, and Druid or similar systems.

• Experience with REST, GraphQL, gRPC, and Protocol Buffers.

• Familiarity with DevOps practices, SLOs, monitoring/observability, on-call responsibilities, capacity planning, and root-cause analysis.

• Experience with scalable algorithms and causal inference.

🏝️ Benefits

• Emphasis on a remote-first work culture.

• Office hubs located in Santa Monica, CA; Nairobi, Kenya; Mexico City, Mexico; Manila, Philippines; and Bangalore, India.

• A diverse and inclusive global team environment.