Senior Staff Machine Learning Engineer
| Company | Altos Labs |
|---|---|
| Location | San Francisco, CA, USA, San Diego, CA, USA |
| Salary | $221000 – $315100 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- M.S. or Ph.D. in Computer Science, or related quantitative field, or equivalent technical experience
- 8+ years software development experience
- Extensive experience with large scale machine learning tools and infrastructure
- Experience applying software engineering practices in a scientific environment, or another environment with similar characteristics
- Excited to design, implement, and evangelize technical and cultural standards across scientific and technical functions
- Proven track record of delivering high quality software
- Skilled at working effectively with cross-functional teams, including research and engineering organizations
- Excellent written and verbal communication skills.
Responsibilities
- Designing, building, and evaluating large-scale machine learning systems including data transformation pipelines, feature stores, distributed training, architecture optimization, model management & serving, etc.
- Motivated to build, deploy, and manage systems to accelerate large-scale machine learning workflows in an integrated, usable framework
- Interested in understanding user needs across a wide range of scientific disciplines, and communicating with users to build systems that they can use productively
- Demonstrated software engineering skills in developing reliable, scalable, performant systems in a cloud environment
- Champion maintainable, scalable, and reusable software engineering techniques and acts as an ambassador to promote effective tools and practices to the research community.
- Mentor software engineers and computational scientists, evangelizing best practices around development tools, CI/CD, and other methods to improve code quality and efficiency.
Preferred Qualifications
- Familiarity with biological data formats, concepts, and computational models is a plus