Engineering Manager, Computer Vision

About this role

This role is hybrid and required in office on Tuesdays and Thursdays.

About Glacier

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.

The Role

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.

What you'll do

• 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.

• What we're looking for
• -
• 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
• What will make you successful
• 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
• Bonus points
• Experience working in an early-stage startup environment (Compensation

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.