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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates strong leadership in managing and developing a team of Machine Learning Engineers, with a focus on hiring, performance management, and technical direction. Proficient in building and operating machine learning systems, ensuring operational excellence, and collaborating across engineering and data teams.
Highest-signal resume keywords
Team ManagementMachine Learning SystemsPython ProgrammingCloud Platforms (AWS, GCP, Azure)Incident Response
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningPythonSQLBackend Software EngineeringScalable AlgorithmsCausal InferenceTechnical ArchitectureSoftware QualityProduction OperationsPerformance Management
Soft Skills
CoachingFeedback ProvisionGoal SettingCollaborationDelegation
Tools & Technologies
JupyterPandasScikit-LearnTensorFlowPyTorchAWSGCPKubernetesDockerKafka
Industry Keywords
Machine LearningData EngineeringIncident ResponseCapacity PlanningObservability
Tech Stack
Tools & technologiesAirflowAWSAzureCassandraDockerGoogle Cloud PlatformGraphQLGRPCKafkaKubernetesMySQLPandasPostgresPythonPyTorchScikit-LearnSparkSQLSwitchingTensorflow
About the role
Key responsibilities & impact- Lead Tala’s ML Platform 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 expectations, provide feedback, and create development plans
- Coach engineers toward growth and promotion while addressing performance gaps
- Set quarterly goals and ensure consistent delivery
- Own prioritization across product roadmap work, run-the-business activities, and operational excellence
- Balance capacity across new development, maintenance, technical debt, and production support
- Improve productivity by reducing context switching and delegating effectively
- Partner with engineers and technical leads to estimate and scope complex work
- Guide development of platforms and frameworks for data exploration, feature development, and ML model training, testing, deployment, and monitoring
- Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems
- Drive testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment practices
- Own and improve SLOs, on-call health, capacity planning, reliability, and incident response
- Review technical designs and drive architectural standards and technical debt reduction
- Collaborate 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 engineering and data teams
Requirements
What you’ll need- 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
- 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
- Experience with Python and SQL
- Experience with machine learning technologies including Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, and Hugging Face
- Experience with AWS, GCP, Azure, Kubernetes, and Docker
- Experience with streaming technologies including Kafka, Kinesis, Beam, Flink, and Spark Streaming
- Experience with batch processing technologies including Airflow and Metaflow
- Experience with databases including MySQL, PostgreSQL, Cassandra, Snowflake, and Druid or similar technologies
- Experience with REST, GraphQL, gRPC, and Protocol Buffers
- Experience with DevOps, SLOs, monitoring/observability, on-call, capacity planning, and root-cause analysis
- Experience with scalable algorithms and causal inference
Benefits
Comp & perks- Remote-first work approach
- Office hubs in Santa Monica, CA; Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India
- Diverse and inclusive global team environment