Bonsai Robotics, Inc. is hiring: Senior Machine Learning Engineer – VLA in San J
Bonsai Robotics, Inc., San Jose, CA, United States
Bonsai Robotics develops affordable, vision-based autonomy that makes off-road equipment smarter, safer, and more productive. We are redefining outdoor autonomy with Bonsai Intelligence, a connected platform that's inspired by biology to see, think, and act with precision like a human. We bring together advanced perception, embodied AI, integrations with equipment manufacturers, and our compact, modular Amiga vehicles to deliver reliable automation to the world's most demanding field operations—reducing costs and increasing operational efficiencies.
About the role
We're looking for a Machine Learning Engineer who can own the full lifecycle of training and deploying end-to-end Vision-Language-Action (VLA) models for outdoor autonomy. You'll build the models that allow any vehicle—from our Amiga platform to heavy off-road equipment—to navigate, act, and adapt in unstructured outdoor environments using raw sensor inputs (cameras, IMU, GPS, LiDAR, and more). We have collected a large dataset of heavy equipment working in the field from deployments and are looking for people who can leverage massive real-world data as well as data from targeted data collection to train a VLA model. This is a high-impact, end-to-end role: you'll touch everything from data pipelines and model architecture to real-world deployment on edge hardware.
What you'll do
Design, train, and iterate on VLA and other learned behavior policy architectures for vehicle control in diverse outdoor environments
Build and maintain robust data pipelines—ingestion, curation, labeling, and versioning—to support reproducible, high-quality training at scale
Develop evaluation frameworks: offline metrics, simulation-based testing, and real-world field validation loops
Optimize models for deployment on edge compute (NVIDIA Jetson and similar), addressing latency, memory, and throughput constraints
Collaborate closely with perception, controls, platform, and field operations teams to integrate learned policies into our full autonomy stack
Instrument and monitor deployed models in production, diagnosing failure modes and feeding insights back into the training loop
Stay current with the rapidly evolving landscape of foundation models for robotics and bring new ideas from research into practice
Qualifications
3+ years applying deep learning to real-world robotics or embodied AI problems using PyTorch, JAX, Ray, or similar frameworks
3+ years building, deploying, and maintaining ML models in production—not just research prototypes
Strong practical experience with behavior cloning, reinforcement learning, or other data-driven control approaches
Familiarity with multimodal model architectures (vision-language models, VLAs, or similar)
Comfort working across the stack: data infrastructure, model training, optimization, and on-device deployment
Experience working with real sensor data (camera, IMU, GPS, LiDAR) in noisy, unstructured environments
Bonus Points For
Experience with flow‑matching policies, action‑chunking transformers, or other recent advances in learned manipulation
avigation policies
Hands‑on work with TensorRT, ONNX, or other model optimization toolchains for edge deployment
Published work at ICRA, IROS, CoRL, CVPR, NeurIPS, ICML, or similar venues
Experience with ROS2 or robotics middleware in production systems
Background in agriculture, construction, mining, or other outdoor/off‑road domains
Bonsai Robotics is building the future of outdoor autonomy. If you want to ship models that drive real machines in real fields—not just run on a cluster—we'd love to hear from you.
Bonsai Robotics is an Equal Employment Opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, national origin, ancestry, age, disability, gender identity or expression, marital status, or any other legally protected status.
The pay range for this role is:
150,000 - 200,000 USD per year (San Jose, CA)
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About the role
We're looking for a Machine Learning Engineer who can own the full lifecycle of training and deploying end-to-end Vision-Language-Action (VLA) models for outdoor autonomy. You'll build the models that allow any vehicle—from our Amiga platform to heavy off-road equipment—to navigate, act, and adapt in unstructured outdoor environments using raw sensor inputs (cameras, IMU, GPS, LiDAR, and more). We have collected a large dataset of heavy equipment working in the field from deployments and are looking for people who can leverage massive real-world data as well as data from targeted data collection to train a VLA model. This is a high-impact, end-to-end role: you'll touch everything from data pipelines and model architecture to real-world deployment on edge hardware.
What you'll do
Design, train, and iterate on VLA and other learned behavior policy architectures for vehicle control in diverse outdoor environments
Build and maintain robust data pipelines—ingestion, curation, labeling, and versioning—to support reproducible, high-quality training at scale
Develop evaluation frameworks: offline metrics, simulation-based testing, and real-world field validation loops
Optimize models for deployment on edge compute (NVIDIA Jetson and similar), addressing latency, memory, and throughput constraints
Collaborate closely with perception, controls, platform, and field operations teams to integrate learned policies into our full autonomy stack
Instrument and monitor deployed models in production, diagnosing failure modes and feeding insights back into the training loop
Stay current with the rapidly evolving landscape of foundation models for robotics and bring new ideas from research into practice
Qualifications
3+ years applying deep learning to real-world robotics or embodied AI problems using PyTorch, JAX, Ray, or similar frameworks
3+ years building, deploying, and maintaining ML models in production—not just research prototypes
Strong practical experience with behavior cloning, reinforcement learning, or other data-driven control approaches
Familiarity with multimodal model architectures (vision-language models, VLAs, or similar)
Comfort working across the stack: data infrastructure, model training, optimization, and on-device deployment
Experience working with real sensor data (camera, IMU, GPS, LiDAR) in noisy, unstructured environments
Bonus Points For
Experience with flow‑matching policies, action‑chunking transformers, or other recent advances in learned manipulation
avigation policies
Hands‑on work with TensorRT, ONNX, or other model optimization toolchains for edge deployment
Published work at ICRA, IROS, CoRL, CVPR, NeurIPS, ICML, or similar venues
Experience with ROS2 or robotics middleware in production systems
Background in agriculture, construction, mining, or other outdoor/off‑road domains
Bonsai Robotics is building the future of outdoor autonomy. If you want to ship models that drive real machines in real fields—not just run on a cluster—we'd love to hear from you.
Bonsai Robotics is an Equal Employment Opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, national origin, ancestry, age, disability, gender identity or expression, marital status, or any other legally protected status.
The pay range for this role is:
150,000 - 200,000 USD per year (San Jose, CA)
#J-18808-Ljbffr