Principal Machine Learning Engineer
$205,000–$230,000 year
On-site · California, United States
Job Summary
Principal Machine Learning Engineer to build scalable ML systems and platforms that impact millions. You will partner with the Data Platform team to standardize patterns, accelerate modelling from problem inception through production, and develop horizontal solutions to scale ML models and processes. Responsibilities include building software with OO design patterns, participating in requirements/design/code reviews, prototyping new technologies, and collaborating with data scientists and software developers to ensure predictive models drive business impact. Required skills include strong coding (Java, C#, Python), experience with big data (Spark, Hadoop, BigQuery, Athena), robust ML infrastructure, modern software tooling (design patterns, CI/CD, Git, testing), cloud tech (AWS Lambda, Google Functions), and messaging systems (Kafka, Pub/Sub, Kinesis, RabbitMQ). Minimum two years ML-related experience and one year in ML engineering; bachelor’s degree in CS/EE or related field (Masters preferred). Extended hours and a dynamic office environment are noted as part of the work environment.
Required Qualifications
- Bachelor’s Degree in Computer Science, Electrical Engineering, or related field
- Generally requires a minimum of two (2) years relevant experience in applied machine learning or machine learning systems/infrastructure, and one (1) year of relevant work experience in machine learning engineering or related fields
- Experience with modern software development tools, systems, and practices (design patterns, CI/CD, git, unit testing, smoke testing, integration testing)
- Experience with big data technologies (Spark, Hadoop, BigQuery, Athena)
- Experience with Docker and Kubernetes
- Experience with messaging technologies (Kafka, Google Pub/Sub, Kinesis, RabbitMQ)
- Experience building machine learning infrastructure following robust software engineering practices
- Bachelor’s Degree in CS or related; Masters preferred
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