Job Description
ABOUT THE ROLE
We are looking for a Junior/Middle Computer Vision Engineer to support high-volume execution across data preparation, model training, and evaluation for an AI team working with large-scale image and video datasets. You will curate and manage annotation workflows, run model training and evaluation jobs, maintain benchmarks, and collaborate with senior engineers on failure-case analysis. The role offers a a clear growth path into applied modeling or MLOps for an early-career engineer eager to build hands-on AI experience.
MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 1 to 3 years of experience in software engineering, data science, machine learning, or a related field;
- Degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience);
- Engineers located in the US must reside in Dallas, TX, and be open to working from the office (onsite);
- Foundational coding skills in Python;
- Foundational understanding of machine learning concepts and workflows;
- Basic knowledge of computer vision principles (e.g., image processing, object detection basics);
- Basic familiarity with cloud environments and compute resources;
- A strong, demonstrable willingness to learn and adapt in a fast-paced, mentorship-driven environment;
- Excellent attention to detail, specifically regarding data quality and documentation;
- Upper-intermediate English level.
WHAT YOU WILL DO
- Curate large-scale image and video datasets, manage labeling processes and workflows, and ensure the highest standards for dataset quality;
- Run model training and evaluation jobs, ensuring experiments are executed smoothly and efficiently;
- Document training results, maintain ongoing evaluation benchmarks, and track model performance over time;
- Collaborate with senior engineers to analyze model failure cases and identify areas for data or algorithmic improvement;
- Take ownership of foundational tasks that support the broader team's AI/ML lifecycle, directly contributing to the speed and success of production deployments.
PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.
