Tala
From Tala's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
What You'll Do Lead & Grow the Team Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels.
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!
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.
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
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
Scores your resume against this job the way resume scanners do: the job's hard and soft skills, searchability, recruiter tips and formatting.
Create a free account to see your score. Your resume stays in this browser until then, and we never store it.
Already a member? Sign in