Manager of Machine Learning Engineering

Tala; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Tala is seeking a Manager, Machine Learning Engineering to lead Tala’s ML Platform team. This player-coach role will drive architecture, reliability, and production ML systems while growing a team of 4–6 engineers.

You will own priorities across the product roadmap, collaborate with Data Science, Data Platform, and Business teams, and focus on real-time inference, streaming data, and scalable model deployment.

Qualifications

• 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
• 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
• At least 3 years of hands-on Python experience
• 6+ years of backend software engineering experience in consumer-scale applications

Responsibilities

• Lead and grow a team of 4–6 Machine Learning Engineers
• Own prioritization across product roadmap work, run-the-business activities, and operational excellence
• Guide architecture and production systems for real-time inference and streaming data
• Drive engineering best practices: testing, automation, observability, deployment
• Collaborate with Data Science, Data Engineering, and Business teams to translate needs into scalable ML platform solutions
• Coach engineers toward growth and promotions and manage performance gaps
• Ensure reliable on-call health, capacity planning, and incident response
• Partner with tech leads to estimate and scope complex work

Skills

Team management

Hiring & mentoring

Performance coaching

SLOs & incident response

Python programming

Backend software engineering

Architecture & system design

Observability

Data platform collaboration

Capacity planning

Tools

AWS

GCP

Azure

Kubernetes

Docker

Kafka

Kinesis

Beam

Flink

Spark Streaming

Airflow

Metaflow

Jupyter

Pandas

Scikit-Learn

XGBoost

TensorFlow

PyTorch

Hugging Face

MySQL

PostgreSQL

Cassandra

Snowflake

Druid

Job description

• 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
• 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
• 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
• 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
• 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

Requirements

• 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

Technical Skills

• 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: