Our client is building vision agents for large venues such as hotels and casinos— powering real-time video analytics and intelligent surveillance across hundreds of camera streams. Our systems run on-prem in some of the largest resorts in Las Vegas, and many more in the pipeline.
They’re a highly technical team shipping deep tech into one of the most operationally demanding and dynamic environments.
The Role
We’re looking for a Forward Deployed ML Engineer who blends strong technical ML/CV ability with comfort deploying systems in the field.
You will own our real-time vision pipelines end-to-end and be the technical face of the client's inside casinos.
This role is not a back-office research job.
You will:
- Ship models into production
- Debug production pipelines at client sites
- Build new ML features ranging from classical ML, computer vision and LLMs
- Work hands-on with GPU servers & multi-camera systems
- Collaborate with customer surveillance teams and distribution partners
If you love solving real-world problems in messy environments, this is your role.
What You’ll Do
- Train, tune, and update/deploy deep learning models at client sites
- Maintain low-latency inference pipelines on-premise using PyTorch, ONNX, and TensorRT and Triton.
- Build training data processing pipelines, QA/QC labeling and coordinate work with our labelling teams
- Work closely with customers and with the product manager to experiment and ship new features
- 2-3 years of experience in machine learning with strong knowledge about not just deep learning but also classical ML (You’re an ML engineer first — someone who can train models, tune them, debug them in the wild, and build the software around them to make them production-ready.).
- Strong skills in Linux, Docker, and shipping models as services.
- Comfortable working in live production environments with minimal supervision.
- A startup mindset — resourceful, adaptable, and excited to work across ML, backend, and DevOps boundaries