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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