Computer Vision Manager - Fully Remote USD - Latin America

Reposted 4 Days Ago
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12 Locations
Remote
Mid level
Artificial Intelligence • Greentech • Robotics
Technology to end waste
The Role
Lead and mentor a remote ML engineering team, improve machine learning models, manage ML infrastructure, and collaborate on product roadmaps.
Summary Generated by Built In

Glacier is a Series A startup based in San Francisco that builds in-house computer vision models to power two core products:

  • a robot that identifies and sorts materials inside recycling facilities

  • an analytics system that tracks recyclables and reports metrics to key stakeholders in the industry.

These technologies are already helping to divert tons of recyclables (literally!) from landfills every day.

We’re thrilled to continue expanding our growing machine learning team — already composed of highly talented engineers across the Americas — by hiring a team lead based in Latin America to manage and mentor a fully remote group of direct reports.

About us:

  • Our founders come from Facebook engineering and Bain consulting.

  • We’re backed by top-tier VCs with extensive technical and industry expertise.

  • We have several machines in production and a robust pipeline of upcoming deployments.

Here's where your expertise comes into play:

We’re looking for a talented machine learning engineer to lead and grow our remote ML team, inheriting a small but high-performing group of engineers already making a strong impact. You’ll help mentor, coordinate, and guide the team as we continue to scale our core technologies.

Your responsibilities:

  • Coordinate the work of a distributed team of ML-engineers, ensuring work is completed on time and at high quality

  • Communicate clearly and effectively with the San Francisco based team to project plan, ensure timelines are met, and iterate on team structure and processes.

  • Drive the performance of our ML models. That includes: building ML infrastructure, improving current model performance, fine tuning our training process, and ensuring we can easily collect high quality training data.

  • Build automation and experimentation into our full ML lifecycle, enabling us to deploy systems and create impact at scale.

  • Coordinate with our labeling team to ensure we’re working with error-free and well curated data

  • Partner with our founders on the long-term product roadmap; we have lots of ideas for what waste-ending technology to build next, and we’d love to hear yours too!

Requirements:

  • 1+ years experience managing a team of software or ML engineers

  • 2+ years experience developing machine learning models in a deep learning framework like Tensorflow/Keras or Pytorch.

  • 3+ years experience of Computer vision model development - especially object detectors is required.

  • Experience with building machine learning infrastructure (training pipelines, hyperparameter tuning, experiment tracking, etc).

  • Strong expertise in Python and hands-on experience in SQL databases.

  • Proficiency with the SciPy ecosystem (numpy, pandas, matplotlib) and distributed computing in frameworks such as Ray.

  • English proficiency as you will be working with a US based team (C1 or higher)

  • Experience working with US companies or clients is a plus

Top Skills

Tensorflow,Keras,Pytorch,Python,Sql,Scipy,Numpy,Pandas,Matplotlib,Ray
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The Company
HQ: San Francisco, CA
93 Employees

What We Do

Glacier's mission is to end waste. Sound ambitious? We agree. But the UN estimates that we only have until 2030 to change our consumption patterns before we do irreversible damage to the environment, so we’re of the opinion that now is the time for big bets.

We’re starting in the world of recycling, which has a huge opportunity for impact. Americans send 1.4 million tons of waste to recycling facilities every week (that’s about 4 Empire State Buildings, or 1.5 Golden Gate Bridges). We’re also really bad at it: 25% of what we put in our recycling bins isn’t even recyclable. These recycling facilities make a living by sorting our jumbled-up waste and they need to do it cheaply and accurately. Otherwise they go out of business and our recycling goes straight to the landfill. Even so, recycling facilities today use processes that are highly manual, expensive, and error prone. We plan to revolutionize the way these facilities use technology, to make them more streamlined, accurate, and profitable - which means more recyclables avoid the landfill, and more of our natural resources are protected.

Our growing team draws from the brightest and most passionate professionals across robotics, manufacturing, software, AI, and market strategy. We’re united by our deep-rooted passion to make a big environmental impact, and we’re looking for other mission-driven, creative thinkers to help us right the ship on this truly global issue.

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