What are the responsibilities and job description for the Sr/Staff AI Engineer position at Ranger Technical Resources?
Job Details
Sr/Staff AI Engineer #2589
Position Summary:
Our partner is a technology company transforming traditional vehicles into autonomous systems for defense and industrial environments, and they are seeking a Sr/Staff AI Engineer to build the perception intelligence that makes those systems reliable outside controlled conditions. Your focus will center on developing multi-view computer vision and 3D perception systems that allow autonomous vehicles to understand complex environments. This role requires direct ownership of perception components from problem definition through deployment, not participation in isolated research or model experimentation. You will design, train, and deploy perception models that operate under noisy sensing, real-time constraints, and safety-critical conditions, working closely with robotics, autonomy, and systems teams to deliver capabilities that hold up in production.
Experience and Education:
Position Summary:
Our partner is a technology company transforming traditional vehicles into autonomous systems for defense and industrial environments, and they are seeking a Sr/Staff AI Engineer to build the perception intelligence that makes those systems reliable outside controlled conditions. Your focus will center on developing multi-view computer vision and 3D perception systems that allow autonomous vehicles to understand complex environments. This role requires direct ownership of perception components from problem definition through deployment, not participation in isolated research or model experimentation. You will design, train, and deploy perception models that operate under noisy sensing, real-time constraints, and safety-critical conditions, working closely with robotics, autonomy, and systems teams to deliver capabilities that hold up in production.
Experience and Education:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience
- Hands-on experience combining computer vision and machine learning in real-world systems
- Experience building multi-view or 3D perception systems for production applications
- Background in computer vision beyond single-camera detection or classification tasks
- Experience deploying machine learning models into production or safety-critical environments
- Experience in training and scaling models using GPU infrastructure and large datasets
- Exposure to real-time performance constraints, latency tuning, and system-level tradeoffs
- An advanced academic or research background is acceptable when paired with real-world system deployment
- Experience working with robotics, autonomy, vehicles, or other physical systems is strongly preferred
- Multi-view vision
- 3D computer vision
- Spatial reasoning
- Spatial geometry
- Depth estimation
- Camera calibration
- Scene alignment
- Computer vision
- Machine learning
- PyTorch
- TensorFlow
- Python
- C
- Distributed training
- GPU-based training
- Large-scale datasets
- Real-time inference
- Performance optimization
- Model deployment
- Perception system architecture
Primary Job Responsibilities:
- Design and implement multi-camera perception pipelines for unified 3D scene understanding
- Develop vision models that reason about depth, distance, and spatial layout
- Owns major perception components and drives implementation while helping to define technical direction, architecture, and mentors multiple engineers
- Fuse multiple camera viewpoints into consistent world representations
- Train large-scale perception models using distributed GPU infrastructure
- Optimize inference latency, memory usage, and system stability
- Own perception components from model design through production deployment
- Analyze and debug model failures using real-world data
- Evaluate how perception uncertainty impacts downstream planning and control
- Translate research ideas into reliable, production-grade systems
- Balance model accuracy with performance and system constraints
- Collaborate closely with robotics, autonomy, and systems engineers
- Communicate tradeoffs between research, performance, and deployment
- Improve robustness of perception systems under degraded sensing conditions
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