The Role
Deploy, maintain, and debug real-time computer vision and ML pipelines on-prem at client sites. Train and tune deep learning and classical ML models, optimize low-latency inference with PyTorch/ONNX/TensorRT/Triton, build data/labeling pipelines, and collaborate with customers and partners.
Summary Generated by Built In
Forward Deployed Engineer (Machine Learning)
Location: Las Vegas, NV / Remote (frequent on-site deployment)
Company: EagleSight.ai
Type: Full-time
About Us:
EagleSight 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 with many more in the pipeline.
We’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 EagleSight 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.
What We’re Looking For:
- 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.
- Experience configuring and deploying AeroStream multiplexers.
Why Join Us:
You’ll be joining a small, fast-moving team where your work directly impacts live systems used in large venues every day. You’ll have ownership over real infrastructure, autonomy to ship fast, and the chance to grow along with a team that has gained strong traction in a short period of time.
Skills Required
- 2-3 years of experience in machine learning
- Strong knowledge of deep learning and classical ML; ability to train, tune, and debug models
- Experience deploying and maintaining low-latency inference pipelines on-prem using PyTorch, ONNX, TensorRT, and Triton
- Strong skills in Linux, Docker, and shipping models as services
- Experience working with GPU servers and multi-camera video systems
- Comfortable working in live production environments with minimal supervision
- Build training data processing pipelines, QA/QC labeling, and coordinate with labeling teams
- Ability to work closely with customers and product managers to experiment and ship features
- Experience with GStreamer, FFmpeg, or RTSP video pipelines
- Experience configuring and deploying AeroStream multiplexers
- Familiarity with LLMs for feature development
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The Company
What We Do
EagleSight.ai develops on-premise Vision AI agents that transform passive CCTV surveillance into proactive profit and security protection. Utilizing advanced machine learning, AI, and computer vision technology, the company enables high-capacity venues, particularly major casino operations, to detect critical incidents, dealer errors, and cheating in real-time, significantly enhancing overall game protection and supporting efficient revenue recovery across their operations.
