Machine Learning Engineer - Computer Vision
New
C
CompanyCamPhoto Documentation App
U.S.Full-TimeMiddle
Salary220,000 - 250,000 USD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonSQLGitPyTorchTensorflow
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.
Responsibilities
- 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.
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