SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 196,000 - 220,000 / annual
Glacier is a Series A startup based in San Francisco building custom sorting robots and AI-powered business analytics to improve recycling and reduce waste. The company's technology has been recognized as one of TIME's Best Inventions and featured in major publications including TechCrunch, Fortune, and CBS.
You will lead Glacier's Computer Vision organization, reporting directly to the Co-Founder and CTO. This is a technical leadership role focused on vision, strategy, and team execution rather than day-to-day coding.
Key responsibilities:
- Own the vision, strategy, and execution of the computer vision roadmap in partnership with Product Management
- Lead, mentor, and develop a distributed CV engineering team, including hiring, performance management, upskilling, and retention
- Set project and task-level priorities to keep engineers focused and unblocked
- Break down complex technical challenges into clear plans and coordinate execution across multiple engineers and workstreams
- Provide technical guidance on models, datasets, compute, evaluation, and production performance
- Establish lightweight processes to improve execution and collaboration
- Partner with Software, Operations, Manufacturing, and Field Engineering to identify and resolve cross-functional dependencies
- Oversee the labeling function, including resourcing, budget, and quality
You should be comfortable getting deep into technical problems and providing credible guidance on computer vision systems, though you won't be expected to directly code.
Requirements:
- 2+ years of engineering management experience
- 2+ years of hands-on computer vision / ML engineering experience
- Firsthand experience training and deploying computer vision models
- Strong technical understanding of model optimization, dataset quality, compute, evaluation, and production performance
- At least intermediate Python and SQL proficiency
- Experience building technical roadmaps and coordinating complex, cross-functional projects
Bonus qualifications:
- Experience in early-stage startup environments (<50 employees)
- Industrial automation, robotics, or real-world physical systems experience
- ML systems deployed at customer sites
- Experience managing or working closely with data labeling teams
- Edge ML or production computer vision experience
