CompanyCam
Machine Learning Engineer- Computer Vision
Remote Machine Learning Engineering role with clear candidate location fit.
PostedJul 9, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Machine Learning Engineer- Computer Vision
Requirements and responsibilities
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What You'll Do
- 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.
These are our non-negotiables:
- Show up: give us your best and have the courage to do difficult but necessary stuff.
- Grow up: be humble, take responsibility, learn continuously, and have a growth mindset.
- Do good: treat your co-workers and customers the way you want to be treated.
- 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.).
Nice-to-haves
- Embeddings, vector databases, and similarity search
- On-device model deployment (e.g., Core ML, TensorFlow Lite)
- MLFlow, Weights & Biases, or similar experiment tracking platforms
- Amazon Bedrock or other cloud ML services
- Ruby on Rails or JavaScript/React (for integration work)
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Location eligibility
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Hiring flow
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