Teleo, a Havoc company, is a robotics company that transforms construction heavy equipment, including loaders, dozers, excavators, and trucks, into autonomous robots for commercial and defense applications. Our technology enables a single operator to supervise and control multiple machines simultaneously, delivering significant productivity gains while improving operator safety and comfort.
Teleo was founded by a team of experienced technology leaders who previously led the development of Lyft's Self-Driving Car program and Google Street View. Teleo recently announced its merger with Havoc AI, a fast-growing defense technology company developing coordinated fleets of autonomous maritime vessels.
This is a unique opportunity to join a team building technology with real-world impact. You will work on cutting-edge 100,000-pound autonomous robots and engineer complex systems at the intersection of hardware, software, robotics, and AI.
Core Responsibilities
- Re-use, adapt and extend existing perception algorithms (such as those commonly used in AV applications) to be applied to Teleo's operational domain and develop novel multi-modal perception algorithms
- (Re-)Train models as Teleo's operational domain evolves
- Develop auto-annotation and auto-labeling tools using SoTA methods, such as VLMs
- Define evaluation protocols that correlate with on-ground performance
- Drive active learning: select the right data
- Integrate tightly with MLOps for continuous deployment
Required Qualifications
- M.S. or higher in Computer Science, Computer Engineering, Robotics, Electrical Engineering, or a related technical field.
- 3+ years in applied ML with large-scale datasets
- Strong Python + PyTorch
- Experience shipping perception or ML systems into production
- Systems thinker: understands data, models, infra as one system
Strong Experience With
- Auto-annotation techniques like VLM-based labeling
- Model evaluation beyond single metrics (failure modes, edge cases)
- Perception tasks: detection, segmentation, depth, tracking
- Multi-modal data (camera, LiDAR, radar)
Nice to Have
- Dataset management & slicing
- Experience with synthetic data or simulation
- Large-scale data pipelines (Parquet, Arrow, object storage)
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