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bedrock · 4 hours ago

Machine Learning Engineer: Perception

San Francisco OR New York

Full-time

Onsite

Senior Level

Bedrock Robotics is bringing autonomy to the construction industry, leveraging expertise from the autonomous vehicle sector. The Machine Learning Engineer: Perception will design and develop advanced perception systems, optimizing models for real-world applications and collaborating with teams to enhance object detection and semantic segmentation.

ConstructionReal EstateSoftware

Responsibilities

Design Early Fusion Architectures: Develop and train state-of-the-art models (e.g., BEV-based transformers) that fuse raw Lidar and Camera data to solve for object detection and semantic segmentation

Tackle "Messy" Physics: Build perception systems robust enough to handle dynamic occlusion (seeing the robot’s own arm/bucket), particulates (dust, snow, rain), and high-vibration conditions

Deploy to the Edge: Optimize models for inference on embedded hardware. You will debug system-level issues, such as sensor calibration drift and latency bottlenecks

Collaborating with other teams to create state-of-the-art representations for downstream use cases

Qualification

Production ML Experience3D Geometry & CalibrationEarly Fusion ExpertiseSOTA Object DetectionPythonC++RustData IntuitionVoxel/Occupancy ExperienceTop-Tier Research

Required

Production ML Experience: Experience taking deep learning models from research to real-world production using PyTorch

3D Geometry & Calibration: You have a deep understanding of SE(3) transformations, homogeneous coordinates, and intrinsic/extrinsic sensor calibration. You understand the math required to project a 3D Lidar point onto a 2D image pixel accurately

Early Fusion Expertise: Practical experience with architectures that fuse modalities at the feature level (e.g., BEVFusion, TransFuser, PointPainting) rather than just fusing final bounding boxes

SOTA Object Detection experience with modern transformer-based architectures (DETR, PETR, etc…) including similar temporal models (PETRv2, StreamPETR, …)

Systems Fluency: You are an expert in Python, but you are also comfortable reading and writing systems code in C++ or Rust. You understand memory management and real-time constraints

Data Intuition: You understand that in robotics, better data alignment often beats a bigger model. You are willing to dig into the data infrastructure to ensure ground truth quality