• Full Time Jobs
  • Lincoln
  • 220.000000 - 250.000000
Invest Nebraska



We’re looking for a Machine Learning Engineer with deep computer vision experience to join our ML team.

Contractors capture millions of jobsite photos through CompanyCam every day. As an ML Engineer, you’ll turn that visual data into structured understanding, building and shipping computer vision systems that power image classification, document detection, segmentation, multimodal embeddings, and more across 70,000+ projects daily.

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.

What You’ll Bring

Must‑Haves

  • 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).

Benefits And Compensation

This is a salaried position at CompanyCam. Our salary range is $220,000 – $250,000 per year and is based on experience. We also offer meaningful equity and other benefits.



Equal Employment Opportunity

CompanyCam is an equal‑opportunity employer committed to respect, inclusion, and growth. We work hard, take responsibility, and support each other. Great ideas come from all backgrounds, and we carefully consider every applicant without regard to personal characteristics or traits. Even if your work experience doesn’t align perfectly, we encourage you to apply. What really matters to us is your potential, your passion, and your commitment to learning, innovation, and contributing meaningfully to our team.

For any accommodations or technical issues related to the online application or interview process, please email [email protected] and we’ll respond promptly. Please do not include any medical or health information in your message.

Note: Resumes sent to this email will not be reviewed or responded to. To be considered for a position, you must apply directly through our careers page.


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