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San Francisco, California, United States
Hybrid
This role is hybrid and required in office on Tuesdays and Thursdays.
Hey, we're Glacier! Series A startup based in San Francisco tackling one of the world's most pressing problems: trash. Did you know that in the US, we send over half of our recyclables to the landfill? We're working to fix that. In doing so, we'll also be reducing carbon emissions, energy consumption, and depletion of natural resources.
Glacier builds custom sorting robots designed to sort apart recyclables as well as AI-powered business analytics that enable recyclers to superpower their plants and improve our society's circularity. From major CPG companies like Colgate and Amazon to municipal recycling facilities, our clients trust us to turn recycling data into actionable insights. Our technology has been recognized as one of TIME's Best Inventions and featured in a TIME documentary, TechCrunch, Fortune, and CBS.
We're looking for an experienced Engineering Manager, Computer Vision to lead Glacier's Computer Vision organization. You'll own the vision and execution of our CV roadmap, lead our international CV engineering team, and ensure the team is focused on the highest-priority work.
This role reports directly to our Co-Founder and CTO and will play a key role in shaping our computer vision strategy, team execution, and cross-functional collaboration as we scale.
Own the vision, strategy, and execution of Glacier's computer vision roadmap in partnership with Product Management
Lead, mentor, and develop our distributed CV engineering team, including hiring, performance, upskilling, and retention
Set project and task-level priorities, keeping engineers focused and unblocked
Break 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 that 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
This is a technical leadership role, not a day-to-day development role. You won't be expected to directly code, but you should be comfortable getting deep into technical problems and providing credible guidance on computer vision systems.
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
Technical depth: You can engage deeply with computer vision problems and guide strong engineers through deeply technical challenges
Prioritization: You can turn ambiguous technical challenges into clear priorities and balance team interests with company needs
Systems thinking: You understand how your team decisions affect Software, Operations, Manufacturing, Field Engineering, and customers
Execution: You break large problems into actionable plans, manage dependencies, and surface risks early
Communication: You're concise, can push back thoughtfully, and adjust when new information changes the right answer
Leadership: You know how to support and challenge a highly autonomous team while keeping people accountable
Experience working in an early-stage startup environment (<50 employees)
Industrial automation, robotics, or other 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
The total cash compensation range for this role is $196,000 - $220,000. In addition to cash compensation, Glacier also offers competitive equity compensation and benefits. Final compensation will depend on job-related skills and knowledge, experience level, interview performance, and other relevant factors.
