Machine Learning Engineer (Computer Vision)

Quick Facts

• Role: Machine Learning Engineer (Computer Vision)

• Focus: End-to-end machine learning for customer-facing computer vision

Description

Build production-ready computer vision features from concept to deployment, translating business requirements into practical ML solutions. Own work across the full machine learning lifecycle—data collection and annotation, dataset management, experimentation, model development and evaluation, deployment, monitoring, and iteration. You’ll also build reusable Python tooling and shared libraries to improve the team’s productivity.

Responsibilities

• Design and deliver production-ready computer vision features from concept to deployment

• Translate product and business requirements into ML solutions

• Manage the full ML lifecycle: collection, annotation strategy, dataset management, experimentation, development, evaluation, deployment, monitoring, and iteration

• Build robust Python tooling and shared libraries

• Develop reliable, maintainable, and well-tested production code

• Collaborate with software engineers and product stakeholders to deliver measurable customer value

• Improve model quality through data-driven experimentation

• Help establish best practices for reproducibility, testing, documentation, and MLOps

Requirements

• 3–5 years of commercial experience building machine learning systems

• Strong ML fundamentals (supervised, unsupervised, self-supervised learning; optimization; probability and statistics; linear algebra; model evaluation/validation)

• Solid understanding of the end-to-end ML lifecycle and practical production challenges

• Strong Python engineering (clean, maintainable, testable code; algorithms and data structures; debugging and performance optimization; reusable libraries/APIs)

• Deep learning model experience with PyTorch

• Understanding that system success depends on data quality, evaluation methodology, and deployment

• Ability to independently own technical problems from definition through production delivery

• Strong communication skills and ability to translate business problems into technical solutions

Benefits

• Direct collaboration with company founders and use of bleeding-edge technology

• Exciting challenges and real impact

• Career growth in a dynamically developing startup

• Friendly startup office environment; hybrid remote work

• Fitness and health membership benefits

• Employee share options program

• Gaming laptop for work

• Company off-sites and team activities