Machine Learning Engineer – Computer Vision

Job Description:

• Design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics.
• Own the full ML pipeline: data preprocessing, feature engineering, model selection, training, evaluation, and deployment into sustainable inference services.
• Conduct discovery spikes to validate feasibility and inform go/no-go decisions before committing to full development.
• Integrate ML solutions with observability tooling, establishing and maintaining benchmarks to measure improvement and compare approaches.
• Build automated, self-sustaining ML pipelines. Models should train, evaluate, and deploy with minimal manual intervention.
• Inform build-vs-buy decisions with both technical rigor and business context, understanding when in-house models create competitive advantage vs. when vendor APIs are sufficient.
• Collaborate with software engineers, data engineers, and product stakeholders to integrate ML solutions into CompanyCam's platform.
• Communicate clearly with non-technical audiences about feasibility, requirements, and trade-offs of proposed solutions.

Requirements:

• 3+ years of experience shipping machine learning models to production (not just training them)
• Experience with computer vision techniques including image classification, segmentation, and object detection
• Strong coding skills in Python with proficiency in PyTorch or TensorFlow and comfort with modern architectures (transformers, CNNs, etc.)
• Strong SQL skills including joins, subqueries, window functions, and CTEs
• Proficiency in data analysis, cleaning, transformation, and feature engineering
• Experience with version control (Git), experiment tracking, and ML development best practices
• Ability to explain technical concepts to non-technical stakeholders through clear writing and presentations
• You live and work permanently in the U.S. (We're not set up to hire outside the U.S.)

Benefits:

• meaningful equity
• and other benefits

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