Principal Machine Learning Engineer
Mission Lane210,000 - 256,000 USD/year
- Full Time
- Junior (1 to 2 years)
Not SpecifiedCompensation
Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Information Technology & ServicesIndustries
Requirements
Candidates should possess a Bachelor’s degree in Computer Science, Engineering, or a related field, and have at least 8 years of experience in designing and building distributed systems and infrastructure, with a strong focus on machine learning. Demonstrated expertise in cloud technologies, particularly AWS, is essential, along with experience with containerization technologies like Docker and Kubernetes. Proficiency in programming languages such as Python and Go is required, alongside a deep understanding of data engineering principles and experience with big data technologies like Spark or Hadoop.
Responsibilities
As a Principal ML Infrastructure Engineer, you will own technical workstreams from start to finish, contribute to the team’s product roadmap, and be responsible for major technical decisions and tradeoffs. You will design, implement, and optimize distributed systems and infrastructure components to support large-scale machine learning workflows, including data ingestion, feature engineering, model training, and serving. Furthermore, you will develop and maintain frameworks, libraries, and tools to streamline the end-to-end machine learning lifecycle, and collaborate with machine learning researchers and data scientists to translate research into scalable software solutions. Finally, you will mentor teammates, conduct code reviews, and uphold engineering best practices to ensure the delivery of high-quality software solutions.
Skills
Machine Learning Infrastructure
ML Systems
ML Deployment
Scalable Infrastructure
Platform Development
Cross-functional Collaboration
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Key Metrics
San Francisco, CaliforniaHeadquarters
2015Year Founded
$143.8MTotal Funding
IPOCompany Stage
Consulting, Enterprise SoftwareIndustries
10,001+Employees