Machine Learning Engineer – Computer Vision

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop, train, and implement computer vision models in production with clear understanding of performance, latency, and cost metrics.

• Take responsibility for the entire ML pipeline: data preprocessing, feature engineering, model selection, training, evaluation, and deployment into robust inference services.

• Execute discovery spikes to assess feasibility and guide go/no-go decisions prior to committing to full-scale development.

• Integrate ML solutions with observability tools, establishing and sustaining benchmarks for measuring improvements and comparing methodologies.

• Create automated, self-sustaining ML pipelines where models can train, evaluate, and deploy with minimal manual oversight.

• Provide insights for build-vs-buy decisions, applying both technical analysis and business context to determine when in-house models offer a competitive edge versus when vendor APIs suffice.

• Work collaboratively with software engineers, data engineers, and product stakeholders to embed ML solutions into CompanyCam's platform.

• Effectively communicate with non-technical audiences regarding feasibility, requirements, and trade-offs of proposed solutions.

⛳️ Requirements

• Over 3 years of experience in deploying machine learning models to production (beyond just training).

• Familiarity with computer vision methodologies such as image classification, segmentation, and object detection.

• Strong programming skills in Python, with expertise in PyTorch or TensorFlow and an understanding of contemporary architectures (transformers, CNNs, etc.).

• Proficient in SQL, including joins, subqueries, window functions, and Common Table Expressions (CTEs).

• Skilled in data analysis, cleaning, transformation, and feature engineering.

• Experience with version control systems (Git), experiment tracking, and ML development best practices.

• Capable of articulating technical concepts to non-technical stakeholders through clear writing and presentations.

• Must reside and work permanently in the U.S. (We are unable to hire candidates outside of the U.S.).

🏝️ Benefits

• Meaningful equity.

• Additional benefits.