Lead Machine Learning Engineer

The Role

The Lead Machine Learning Engineer is an individual contributor and a technical lead who will build, monitor, and maintain Tala’s core machine learning and causal inference services and tooling. You will own customer-facing real-time streaming feature extraction and model inference, model-related batch compute platforms and jobs, service level objective definition and measurement, root cause analysis, software and architecture design, enterprise technical maturity assessment, highly effective cross-functional collaboration, and mentorship.

What You'll Do

• Develop Data Scientist and Analyst-friendly self-service tooling and frameworks to explore new data sources, extract new features, and train, test, deploy, and monitor models
• Optimize the model development and software development life cycles
• Maximize quality of models, services, and tooling with unit testing, integration testing, dry run and blue-green deployment, infrastructure-as-code, automation, observability, and fault tolerance
• Write and review design documents, perform code reviews, and weigh in on implementation choices from other technical teams
• Collaborate with and support cross-functional teams (Product, Data Platform, Credit, and Business Development)

What You'll Need

• 6+ years backend software experience in consumer scale applications, at least 4 of them with Python
• 2+ of those years in real-time streaming data (Kafka, Kinesis, Beam, Flink, Spark Streaming)
• 2+ of those years in a tech lead role
• Proficiency with machine learning tools and tech (Jupyter, Pandas, Scikit-Learn, Xgboost, Tensorflow, Pytorch, Hugging Face
• Strong database experience, both relational and non-relational (MySQL, PostgreSQL, Cassandra, HDFS, Snowflake, Druid)
• Strong hands-on experience in cloud computing (AWS, GCP, Azure, Kubernetes)
• Experience with batch processing platforms (Airflow, Metaflow)
• Experience autonomously building machine learning or causal inference models to solve business problems