Staff Machine Learning Engineer
Gridware
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
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Job Level
About the role
- Design, implement, and deploy advanced machine learning models operating on multi-modal and multi-resolution data
- Lead development of algorithms that improve the speed, accuracy, and reliability of Gridware’s automated hazard detection systems
- Define data strategy and labeling requirements, including real-world data collection and synthetic data generation approaches
- Partner with software engineering and ML infrastructure teams to ship robust, production-grade ML systems
- Act as a technical leader and reviewer across Machine Learning and Data Science teams, influencing architecture and modeling decisions
- Mentor and support junior engineers and scientists through design reviews, pairing, and technical guidance
- Explore and evaluate novel modeling approaches and research ideas to address existing and emerging automation challenges
Requirements
- 8+ years of experience building and deploying machine learning models in production environments
- Deep experience with both deep learning and classical machine/statistical learning techniques
- Strong programming skills with demonstrated proficiency in Python
- Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows
Benefits
- Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
- Paid parental leave
- Alternating day off (every other Monday)
- “Off the Grid”, a two week per year paid break for all employees.
- Commuter allowance
- Company-paid training
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningdeep learningclassical machine learningstatistical learningPythonalgorithm developmentdata strategydata labelingsynthetic data generationproduction-grade ML systems
Soft skills
technical leadershipmentoringdesign reviewstechnical guidancecollaboration