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Tala is an AI-native credit infrastructure platform serving the global majority, having distributed over $7 billion in capital to 13+ million customers across Africa, Latin America, and Asia. The company combines proprietary risk intelligence with a network of capital and distribution partners to power credit access at scale.
As Manager of Machine Learning Engineering, you will lead and grow a team of 4–6 Machine Learning Engineers at mid-to-senior levels. Your responsibilities span three core areas:
**Team Leadership & Development:** You'll manage hiring, sourcing, interviewing, and closing strong ML engineering talent. You'll establish clear expectations, provide regular feedback, and create development plans for direct reports. You'll coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully, and create opportunities for engineers to take on challenging projects and develop technical leadership.
**Engineering Delivery:** You'll set quarterly goals and ensure consistent delivery against them. You'll own prioritization across product roadmap work, run-the-business activities, and operational excellence. You'll balance team capacity across new development, maintenance, technical debt, and production support, improve team productivity by reducing context switching and delegating effectively, and partner with engineers and technical leads to estimate and scope complex work.
**Technical Leadership:** You'll 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. You'll provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems. You'll drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment. You'll own and improve SLOs, on-call health, capacity planning, reliability, and incident response, and review technical designs to drive architectural standards and technical debt reduction.
**Cross-Functional Collaboration:** You'll work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams. You'll translate business and technical needs into scalable ML platform solutions, coordinate dependencies and delivery across multiple engineering and data teams, and help create structure and clarity in an environment where priorities and requirements can evolve.
Tala operates on a remote-first approach with office hubs in Santa Monica, CA (HQ), Nairobi, Kenya, Mexico City, Mexico, Manila, the Philippines, and Bangalore, India.
**Requirements:**
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 (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
