Description
The Senior Machine Learning Engineer is an individual contributor 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.
The Senior Machine Learning Engineer is an individual contributor 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
5+ years of backend software experience in consumer scale applications, at least 4 of them with Python2+ of those years in real-time streaming data (Kafka, Kinesis, Beam, Flink, Spark Streaming)Experience autonomously building machine learning or causal inference models to solve business problems.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 with API development for mobile/web use (REST, GraphQL, gRPC, Protocol Buffers).Strong collaboration experience with Data Science, Analytics, other Engineering teams, and business stakeholders.Knowledge of scalable algorithms.Prefer advanced degree in computer science, math, or related field.
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