Forward Deployed Engineer (Machine Learning)

Job Description

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

Requirements

• 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.

Nice to Have

• Experience with GStreamer, FFmpeg, or RTSP (or similar protocol) video pipelines.
• Experience with Triton Server, model optimization using TensorRT and other deep learning acceleration frameworks.

Benefits

• Work remotely Monday - Friday, 40 hours a week (no weekends)
• Vacation: 10 business days a year
• Holidays: 5 National Holidays a year
• Company Holidays: 5 Company Holidays a year (Christmas Eve, Christmas Day, New Year's Eve, New Year's Day, Zipdev Day)
• Parental Leave
• Health Care Reimbursement
• Active Lifestyle Reimbursement
• Quarterly Home Office Reimbursement
• Payroll Deduction Purchase Plans
• Longevity Bonus
• Continuous Learning Bonus
• Access to Training and Professional Development Platforms
• Did we mention it's REMOTE?!!

One of our core values at Zipdev is "Be authentic." that's why we encourage you to answer the application form in your own words; we are interested in getting to know you, not a digital assistant.

Wondering how our remote environment or our payment method work? We've put together some helpful answers in our FAQs at the bottom our our career site. Take a look and let us know if you have any other questions!