Position Summary:

As a Senior Machine Learning Enigneer, you will build out our vision of a novel, best-in-class MLOps platform to fuel our personalized & data driven healthcare data products and pipelines that power our Powered by League platform. You will help architect and support an MLOps Platform with the primary goal of empowering data scientists to deploy, train, host, and evaluate their models in a self-serve manner, providing valuable insights on its performance and continued refinements. The MLOps platform will be a highly available, secure and governed end to end system built on cloud infrastructure. 

In this role, you will work as part of a multidisciplinary team that include the Data Platform, Personalization and Research & Insights teams to establish and evangelize a data and machine-learning driven culture. This team will not only work with the product team closely but will also support non-engineering functions like Marketing and Business Strategy.

To thrive in this role, you are someone who works well in cross-functional teams and enjoys collaborating. Furthermore, you understand the business impact of your work and enjoy measuring and presenting it. You enjoy working with product managers, data scientists, data analysts, data engineers and other stakeholders to find the best solution to the problem at hand, iterate over it and can balance technical complexity with delivering customer value.

Our platform and applications run on Google Cloud. You will be working on building infrastructure to launch the MLOps platform that serves both real-time and batch machine learning pipelines that ingest, split, test, train, re-train and monitor models based on data from a variety of sources. You will have an opportunity to experiment with new frameworks and paradigms, and freedom to put cutting-edge tech in production to shape the future of digital health!

In this role you will:

• Drive architectural choices and develop the League set of MLOps platform tools.
• Guide and mentor data scientists and engineers through the MLOps process and framework, including mentoring data scientists in areas such as software development, lifecycle, & data engineering best practices.
• Engage in discourse with Data Scientists on trade-offs of deploying various data science models in production.
• Translate business and stakeholder needs into MLOps requirements, with attention to details.
• Utilize a variety of distributed computing frameworks and cloud services and tools to build scalable ML pipelines and endpoints.
• Analyze, tune, troubleshoot and support the MLOps platform ensuring the performance, integrity, and security of data and models produced.
• Use sound agile development practices (testing and code reviewing, etc.) to develop and deliver data products.

About you:

• Minimum 5 years experience in data science, software engineering, data engineering or related discipline.
• Ability to articulate pros and cons of technical decisions and influence stakeholders.
• Strong experience with a suite of cloud DevOps and CI/CD tools (Terraform, Docker, CircleCI, GitHub Actions, Cloud Build, etc) and processes.
• Strong Experience with distributed data processing frameworks such as Apache Beam (ie DataFlow), Spark, Flink or similar.
• Experience with multiple programming languages – Required: Python, SQL, Nice to Have: Scala, Go, R,  C/C++ etc.
• Experience with GCP VertexAI, Azure Machine Learning Studio or AWS SageMaker
• Experience with orchestration tools such as Apache Airflow.
• Experience in developing real time (RabbitMQ, Kafka, Pub/Sub) and batch pipelines. 
• Experience with developing, implementing, deploying and scaling machine learning  models to production.
• Experience in performing root cause analysis of production issues, performance tuning and optimization.
• Experience using and extending ML frameworks and libraries (e.g. TensorFlow, PyTorch,  scikit-learn, SHAP).
• Experience in healthcare datasets like EMR and Claims and interoperability standards like FHIR.

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