Duration: 4 Month contract (W2) with strong potential for extension
Location: Sunnyvale, CA.
No C2C/CORP TO CORP/1099
Summary:
We’re looking for a ML Infrastructure Software Engineer with a strong background in web application development and back end systems, passionate about building reliable applications and intuitive tools that accelerate user productivity & UX.
Description:
As a Software Engineer on the ML Lifecycle team, you’ll work on the systems that power the full ML lifecycle—from data ingestion and labeling to model training, deployment, and monitoring . You'll design and build user-facing tools for ML practitioners as well as robust backend services that enable scalable ML operations. You’re passionate about writing clean, maintainable code and enjoy collaborating with cross-functional teams, including ML engineers and platform engineers. You are comfortable with best practices in software engineering including agile development, code reviews, CI/CD pipelines, and automated testing.
Minimum Qualifications:
• 5-7 years of experience in the ML/ AI domain
• Proficiency in backend development using Python and FastAPI for building RESTful services.
• Experience with data and object stores (e.g., PostgreSQL, Redis, S3) in production systems.
• Strong experience building web applications (Front End) with React, Typescript/JavaScript, HTML, and CSS.
• Ability to translate ML team requirements into scalable tools and interfaces.
• Familiarity with ML workflows and developer ergonomics for ML practitioners.
Top 3 skills:
Back end – Python, REST API, FastAPI - required
Coding – refactoring code – Python
Ecosystem with deploying a production app scale services – AWS tools, familiarity with production-based systems
Front end - React/TypeScript/JavaScript – minimal – not more than 20%
Bonus – infrastructure
Deploy applications
Look at existing code and make it more efficient (mostly Python)
Python, Java would be good
Object stores, hands on experience with production preferred
Industry:
AI/ML exposure would be great
Preferred Qualifications:
• Experience with event-driven architectures and messaging systems (e.g., Kafka).
• Exposure to container orchestration and cloud-native deployment (Kubernetes, Docker, Helm).
• Experience deploying infrastructure on AWS, especially with infrastructure-as-code tools like Pulumi.
• Experience setting up documentation portals (e.g., Docusaurus) for internal developer tools.
• Familiarity with observability best practices (metrics, logging, alerting) for ML systems.
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