Machine Learning Engineer - Computer Vision
Invest Nebraska is a venture capital organization that provides financial and operational assistance to high-growth companies in Nebraska. It advances the state's entrepreneurial economy and attracts out-of-state capital to fund early-stage, product-market-fit companies across all industries.
About Invest Nebraska
Invest Nebraska builds a better future for Nebraska by providing financial and operational assistance to high growth companies, advancing the entrepreneurial economy, and attracting out-of-state capital to the state. Since 2002, Invest Nebraska has been a cornerstone of Nebraska's entrepreneurial ecosystem, investing in companies with early signs of product-market fit, with an average first check of $250k, across all industries, located in Nebraska. The organization has made 155+ high growth investments, helped raise $532.11M+ in follow-on capital, deployed $44.95M+ in capital, created 1,215+ jobs, matched $215M+ in private capital, and its portfolio companies have generated $545.85M+ in cumulative revenue. Invest Nebraska's team includes an experienced staff, dedicated board, and passionate partners committed to building world class businesses with Nebraska entrepreneurs. Its portfolio includes companies such as CompanyCam, Lifeloop, Ocuvera, Marble, BasicBlock, Workshop, and Nobl.
Skills
About the Role
You'll join a small ML team turning millions of daily jobsite photos into structured understanding. You'll design, train, and deploy computer vision models to production with well-understood performance, latency, and cost characteristics. You'll own problems end-to-end, from data preprocessing and model training through evaluation and production deployment, making architectural decisions rather than just tuning hyperparameters on someone else's model. You'll conduct discovery spikes to validate feasibility, integrate ML solutions with observability tooling, and build automated, self-sustaining ML pipelines that train, evaluate, and deploy with minimal manual intervention. You'll also inform build-vs-buy decisions and collaborate closely with software engineers, data engineers, and product stakeholders, communicating clearly with non-technical audiences about feasibility, requirements, and trade-offs.
Requirements
- 3+ years of experience shipping machine learning models to production
- 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
- Live and work permanently in the U.S.
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 that train, evaluate, and deploy with minimal manual intervention
- Inform build-vs-buy decisions with technical rigor and business context
- Collaborate with software engineers, data engineers, and product stakeholders to integrate ML solutions into the platform
- Communicate clearly with non-technical audiences about feasibility, requirements, and trade-offs of proposed solutions
Benefits
- Meaningful equity