NOTE: This opportunity is with one of our venture companies building advanced surveillance and player tracking technology designed specifically for casinos.

ABOUT THE OPPORTUNITY:

We are looking for a Lead ML Engineer accountable for developing and optimizing machine learning models, focusing on poker chip detection, player tracking, and game analytics. This role includes providing technical hands-on leadership to a small team of ML engineers, while collaborating with other functions to deliver high quality results in a fast paced startup environment.

QUALIFICATIONS:

• 5+ years of experience in computer vision and ML.
• Advanced degree in ML, AI, or related field.
• Proven track record with object detection/segmentation models (YOLO, Mask R-CNN).
• Strong coding skills in Python and experience with ML frameworks (TensorFlow/PyTorch).
  About the Studio:
  Drive Ventures, Optimize Operations with AltaML’s Venture Studio!
  AltaML’s Venture Studio builds and invests in early-stage artificial intelligence (AI) and machine learning (ML) companies. The Studio helps founders and startups fast-track their journey from zero to one by providing technical expertise, managing operational details, and incorporating insights from our corporate labs to build innovative applied AI ventures. By bringing together talent, capital, and expertise in AI/ML, the Studio elevates ideas with a competitive edge and transforms them into high-performing companies.
  Whether the concept originates from founders, internal ideation or the services side of the business, the Studio catalyzes venture inception - evaluating and selecting ideas that will drive transformative change and unlock boundless human potential. The team is dedicated to identifying exceptional founders and collaborating with them to nurture these groundbreaking ventures, propelling their advancement toward Series A

• Design and deploy computer vision models for object detection and segmentation.
• Lead the development of image and video processing algorithms.
• Collaborate with software engineers and product managers to integrate ML models into product architecture.
• Mentor junior ML engineers and lead ML design decisions.
• Optimize models for on-premise deployment with minimal latency.