Job description

About Tala Tala is the AI-native credit infrastructure that connects global capital to the global majority. We combine proprietary risk intelligence with an expanding network of capital and distribution partners to power credit access at scale. To date, Tala has distributed more than $9 billion in capital to more than 14 million customers across Africa, Latin America, and Asia, building the definitive contextual dataset on thin-file borrowers in emerging markets. Tala is now converting that foundation into shared infrastructure that partners, capital providers, and ecosystems can build on. The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running. Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India. Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you! The Role We’re looking for a Manager, Machine Learning Engineering to lead Tala’s ML Platform team. This person will manage a team of Machine Learning Engineers responsible for building the platforms, frameworks, and infrastructure that enable our Data Science teams to securely train, deploy, monitor, and operate machine learning models at scale. This is a player-coach management role. You’ll be responsible for developing and growing the team while also providing enough technical leadership to guide architecture, engineering practices, reliability, and production systems. The role has a particular focus on real-time machine learning inference and streaming data systems, as well as the platforms that support batch model development and deployment. What You'll Do Lead & Grow the Team Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels. Hire, source, interview, and close strong MLE talent. Establish clear expectations, provide regular feedback, and create development plans for direct reports. Coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully. Create opportunities for engineers to take on challenging projects and grow their technical leadership. Own Engineering Delivery Set quarterly goals and ensure the team consistently delivers against them. Own prioritization across product roadmap work, run-the-business activities, and operational excellence. Balance team capacity across new development, maintenance, technical debt, and production support. Improve team productivity by reducing context switching and delegating effectively. Partner with engineers and technical leads to estimate and scope complex work. Provide Technical Leadership 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. Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems. Drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment. Own and improve SLOs, on-call health, capacity planning, reliability, and incident response. Review technical designs and help drive architectural standards and technical debt reduction. Partner Across the Organization Work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams. Translate business and technical needs into scalable ML platform solutions. Coordinate dependencies and delivery across multiple engineering and data teams. Help create structure and clarity in an environment where priorities and requirements can evolve. What You'll Need 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 We’re 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 Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.

Requirements

Must-haves

Lead & Grow the Team
Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
Hire, source, interview, and close strong MLE talent.
Establish clear expectations, provide regular feedback, and create development plans for direct reports.
Coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully.
Create opportunities for engineers to take on challenging projects and grow their technical leadership.
Own Engineering Delivery
Set quarterly goals and ensure the team consistently delivers against them.
Own prioritization across product roadmap work, run-the-business activities, and operational excellence.
Balance team capacity across new development, maintenance, technical debt, and production support.
Improve team productivity by reducing context switching and delegating effectively.
Partner with engineers and technical leads to estimate and scope complex work.
Provide Technical Leadership
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.
Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems.
Drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment.
Own and improve SLOs, on-call health, capacity planning, reliability, and incident response.
Review technical designs and help drive architectural standards and technical debt reduction.
Partner Across the Organization
Work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams.
Translate business and technical needs into scalable ML platform solutions.
Coordinate dependencies and delivery across multiple engineering and data teams.
Help create structure and clarity in an environment where priorities and requirements can evolve.
What You'll Need

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimachine learningmlpythonsqlawsgcpazurekubernetesdockerrestgraphql

Platform: lever
Effort: quick

Benefits

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Vision

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