Develop, optimize, and deploy production-grade computer vision and deep learning models for real-time perception and navigation on edge hardware. Integrate AI systems with robots, drones, unmanned systems, and swarms while optimizing inference with CUDA and TensorRT. Manage containerized deployments using Docker and Kubernetes, contribute to CI/CD and automated testing, and transition research prototypes into reliable production features.
Who we are
At Latent AI, we are pushing the boundaries of edge computing and artificial intelligence. We specialize in bringing high-performance, low-latency AI to the edge, enabling real-time decision-making in autonomous systems and robotics. We are looking for a passionate and driven AI/ML Engineer to join our team and help us build the next generation of intelligent, physical AI systems.
Who we're looking for
We are seeking an AI/ML Engineer with a strong background in computer vision and robotics. In this role, you will be responsible for building, optimizing, and deploying production-grade machine learning systems directly onto edge hardware. You will sit at the intersection of deep learning and robotics, ensuring that our real-time detection models operate with ultra-low latency during high-speed field operations.
If you are obsessed with inference optimization, edge deployments, and making robots smarter, we want to hear from you.
What you’ll do- Model Development: Design, train, and deploy deep learning models for computer vision using PyTorch, focusing on real-time object detection and spatial awareness.
- Inference Optimization: Maximize model inference speed and guarantee low-latency performance on edge hardware utilizing CUDA and TensorRT.
- Robotics Integration: Seamlessly integrate advanced computer vision models for perception and navigation for robots, drones, unmanned systems, swarms, etc.
- Edge Deployment: Manage, scale, and maintain containerized edge deployments across distributed hardware using Docker and Kubernetes.
- Performance Engineering: Drive automated data insights and high-performance edge computing initiatives to deliver measurable accuracy gains and operational efficiency in complex, high-speed field environments.
An ideal candidate will be someone with;
- Technical depth in ML/AI with a preference for computer vision and robotics. Preference on experience in modality including EO/IR, Acoustic / SONAR, RF / SAR.
- Proven contributions, whether in industry or academia, that moved innovative ideas closer to product. 3-5 years industry experience
- Hands-on execution: the ability to design, build, and ship, not just theorize.
- Collaborative independence: you’re comfortable making autonomous progress, but you thrive in a team environment.
- Growth mindset: curiosity, adaptability, and willingness to learn new approaches and tools.
- Are fluent in Python and modern ML frameworks like PyTorch; familiarity with agentic AI frameworks is a bonus.
- Know your way around CI/CD, automated testing, and documentation, and care about building robust systems.
- Have some familiarity with data science front-end/UI workflows to support quick demonstrations (streamlit, dash, gradio).
- Have a track record of driving innovation from research or prototypes into production ready features or products
- US Citizen requirement due to nature of our projects.
- Candidates based in NJ or Washington DC preferred, remote/hybrid possible.
- We offer health, vision & dental benefits, a 401(k) plan.
Similar Jobs
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software • Defense Technology
Leads Shield AI’s global partner program across reseller and OEM relationships. Responsibilities include recruiting and onboarding strategic partners, designing program structures and incentives, developing two-way channels, enabling partners, governing relationships, and tracking pipeline, revenue, margins, productivity, and incentive ROI. The role systematizes partner operations across regions and requires approximately 40% international travel.
Healthtech • Telehealth
Provides virtual, patient-centered obstetric nursing care during weekend shifts. Conducts clinical and psychosocial assessments, develops individualized care plans, supports wellness goals, addresses care barriers, monitors diagnostic results, triages urgent concerns, educates patients on remote monitoring equipment, documents interactions, and coordinates escalation with multidisciplinary teams. Requires a BSN-prepared RN with an active compact license and recent maternal-child health experience, including at least three years in obstetrics.
Top Skills:
Asynchronous ChatRemote Patient Monitoring EquipmentTelephoneVideo Conferencing
Healthtech • Telehealth
Provides overnight virtual triage and patient-centered clinical care for maternal-child health patients. Responsibilities include clinical and psychosocial assessment, identifying care barriers, reducing care gaps, educating patients on remote monitoring equipment, responding to abnormal results, triaging urgent concerns, documenting care, and escalating to multidisciplinary teams. The role requires three 12-hour overnight shifts weekly, including weekends and rotating holidays, with occasional travel.
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
