Lead Perception Engineer

Albert Invent; Palo Alto (CA); On-site; USD 110,000 - 140,000; Full time

Job summary

Teleo, a robotics company in Palo Alto, seeks a skilled Machine Learning Engineer to develop perception algorithms for autonomous heavy machinery. Join a diverse team committed to making a real-world impact in construction and defense applications.

The ideal candidate has a Master's or higher in a related technical field and at least 3 years in applied machine learning. This position offers a unique opportunity to work with cutting-edge technology and contribute to innovative AI systems.

Qualifications

• M.S. or higher in a relevant technical field.
• 3+ years of experience in applied ML with large-scale datasets.
• Strong knowledge of Python and PyTorch.
• Experience in shipping production ML systems.

Responsibilities

• Adapt and extend existing perception algorithms for operational use.
• Develop evaluation protocols and drive active learning based on data.
• Integrate MLOps for continuous deployment of models.

Skills

Python

PyTorch

Applied ML with large-scale datasets

Auto-annotation techniques

Model evaluation methods

Systems thinking

Perception tasks (detection, segmentation)

Multi-modal data (camera, LiDAR, radar)

Education

M.S. or higher in Computer Science, Robotics, or related field

Job description

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.

Build perception algorithms and architecture, fusing multiple sensor modalities and supporting an evolving set of novel object classes, in order to provide safe operations for heavy machinery working on construction and mining sites.

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)

Teleo is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. All qualified people are encouraged to apply.