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Senior MLOps Engineer

Bonsai Robotics · San Jose, CA, United States · On Site · Active · $120,000–$180,000 / year · Rippling ATS

Job facts

FieldValue
CompanyBonsai Robotics
TitleSenior MLOps Engineer
Normalized title-
Department / teamSoftware Engineering
LocationSan Jose, CA, United States
Work modelOn Site
Employment typeFull Time
Salary$120,000–$180,000 / year
Statusactive
ATS providerRippling ATS
Posted / first seen2025-12-02 / 2026-05-29
Changed / last seen2026-06-06 / 2026-06-06

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City jobsActive postings in San Jose.Open
Department jobsActive postings in Software Engineering.Open
Work model jobsActive On Site postings.Open
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Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyBonsai Robotics
Source8c01e6bc-fe4f-4d9c-98eb-2a3758bfbfed
ATS providerRippling ATS

Description

company About Bonsai Robotics 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. role About the role We’re looking for an MLOps engineer who thrives in real-world robotics environments and can own the entire machine learning lifecycle—from data ingestion and labeling to training, evaluation, and performance monitoring. You’ll support a perception stack spanning 2D / 3D object detection, semantic and instance segmentation, depth estimation, and multi-sensor fusion across camera and lidar. This role is deeply cross-functional: you’ll work with perception engineers, autonomy engineers, field operations, and external labeling teams. The work is fast, tangible, and impacts every vehicle that goes into the field. What you'll do ML Infrastructure & Data Pipelines Build and maintain scalable data pipelines for 2D/3D detection, segmentation, instance segmentation, and depth estimation Develop data workflows across multi-camera systems and lidar stored in MCAP format Own dataset versioning, metadata tracking, and reproducibility systems. Improve training throughput using distributed systems (Ray, PyTorch Lightning, custom launchers). Optimize data formats and loaders for large-scale vision and lidar datasets. Data Curation & Quality Management Build automated tools for dataset selection, active learning, hard-sample mining, and outlier detection. Maintain dashboards and automated checks for dataset health, label quality, class balance, and environment coverage. Partner with field teams to prioritize data collection runs and close the loop between field issues and dataset refreshes. Labeling Operations Manage internal labelers and external labeling vendors. Define annotation standards for camera and lidar tasks. Build QA workflows, reviewer interfaces, and automated label-consistency checks. Identify systematic labeling errors and drive corrective processes. Deployment & Model Lifecycle Build pipelines for continuous evaluation using telemetry from vehicles in the field. Monitor model drift, identify edge cases, and manage regression tests across “golden” datasets. Track on-vehicle performance signals to flag data needs, degradations, or unexpected behavior. Cross-Functional Collaboration Work closely with perception engineers on calibration, sensor models, data schemas, and on-vehicle inference constraints. Coordinate with autonomy and perception teams to align ML outputs with navigation needs. Work with platform team to integrate ML pipelines into core platform infrastructure Partner with fleet operations to review real-world performance and prioritize new data collection. Qualifications 4–7+ years industry experience in MLOps, ML infrastructure, data engineering or applied ML engineering Strong Python development skills. Experience building robust data pipelines for large-scale vision or lidar datasets. Experience managing and operating cloud infrastructure (e.g., AWS EC2, S3, IAM, autoscaling, spot fleets). Familiarity with ML lifecycle tooling (MLflow, Weights & Biases, Metaflow, Airflow, Ray, etc.). Experience managing labeling workflows or working directly with annotation vendors. Strong debugging instincts across the full stack—from data issues to training failures to evaluation anomalies. Bonus Points For Experience with PyTorch, CUDA, and common CV/3D libraries. Experience with multi-sensor fusion, BEV architectures, or 3D perception. Familiarity with MCAP, ROS2, Foxglove, and real-time robotics systems. Experience with autonomous vehicle pipelines or industrial/agricultural robotics. Background in active learning or automated label-quality scoring. Experience building synthetic data augmentations or simulator-driven dataset expansion. Experience building auto-labeling pipelines Bonsai Robotics is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status or any other category protected by law.

Full job record

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Source ID8c01e6bc-fe4f-4d9c-98eb-2a3758bfbfed
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TitleSenior MLOps Engineer
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Statusactive
Activeyes
Location TextSan Jose, CA, United States
DepartmentSoftware Engineering
Team—
Employment Typefull_time
Workplace Typeon_site
Remote Policy—
CountryUnited States
RegionCA
CitySan Jose
Salary RawUSD 120000-180000 YEAR
Salary Min120,000
Salary Max180,000
Salary CurrencyUSD
Salary Periodyear
Source URLhttps://ats.rippling.com/bonsairoboticsmain/jobs/b0da41b6-51b5-406d-b09b-0984640b3326
Apply URLhttps://ats.rippling.com/bonsairoboticsmain/jobs/b0da41b6-51b5-406d-b09b-0984640b3326
First Seen At2026-05-29 07:14:10Z
Last Seen At2026-06-06 08:44:54Z
Last Checked At2026-06-06 08:44:54Z
Last Changed At2026-06-06 08:44:54Z
Inactive At—
Source Posted At2025-12-02 19:21:21Z
Source Updated At—
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      "role": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:18pt;white-space:pre-wrap;\">About the role</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">We’re looking for an MLOps engineer who thrives in real-world robotics environments and can own the entire machine learning lifecycle—from data ingestion and labeling to training, evaluation, and performance monitoring. You’ll support a perception stack spanning 2D / 3D object detection, semantic and instance segmentation, depth estimation, and multi-sensor fusion across camera and lidar.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">This role is deeply cross-functional: you’ll work with perception engineers, autonomy engineers, field operations, and external labeling teams. The work is fast, tangible, and impacts every vehicle that goes into the field.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:18pt;white-space:pre-wrap;\">What you'll do</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px 0px 0px 40px;\"><b><strong style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">ML Infrastructure &amp; Data Pipelines</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build and maintain scalable data pipelines for 2D/3D detection, segmentation, instance segmentation, and depth estimation</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Develop data workflows across multi-camera systems and lidar stored in MCAP format</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Own dataset versioning, metadata tracking, and reproducibility systems.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Improve training throughput using distributed systems (Ray, PyTorch Lightning, custom launchers).</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Optimize data formats and loaders for large-scale vision and lidar datasets.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Data Curation &amp; Quality Management</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build automated tools for dataset selection, active learning, hard-sample mining, and outlier detection.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Maintain dashboards and automated checks for dataset health, label quality, class balance, and environment coverage.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Partner with field teams to prioritize data collection runs and close the loop between field issues and dataset refreshes.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Labeling Operations</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Manage internal labelers and external labeling vendors.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Define annotation standards for camera and lidar tasks.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build QA workflows, reviewer interfaces, and automated label-consistency checks.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Identify systematic labeling errors and drive corrective processes.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Deployment &amp; Model Lifecycle</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build pipelines for continuous evaluation using telemetry from vehicles in the field.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Monitor model drift, identify edge cases, and manage regression tests across “golden” datasets.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Track on-vehicle performance signals to flag data needs, degradations, or unexpected behavior.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Cross-Functional Collaboration</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Work closely with perception engineers on calibration, sensor models, data schemas, and on-vehicle inference constraints.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Coordinate with autonomy and perception teams to align ML outputs with navigation needs.</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Work with platform team to integrate ML pipelines into core platform infrastructure</span></li><li style=\"color:rgb(0,0,0);font-size:11pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Partner with fleet operations to review real-world performance and prioritize new data collection.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:18pt;white-space:pre-wrap;\">Qualifications</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">4–7+ years industry experience in MLOps, ML infrastructure, data engineering or applied ML engineering</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Strong Python development skills.</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Experience building robust data pipelines for large-scale vision or lidar datasets.</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Experience managing and operating cloud infrastructure (e.g., AWS EC2, S3, IAM, autoscaling, spot fleets).</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Familiarity with ML lifecycle tooling (MLflow, Weights &amp; Biases, Metaflow, Airflow, Ray, etc.).</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Experience managing labeling workflows or working directly with annotation vendors.</span></li><li style=\"font-size:12pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Strong debugging instincts across the full stack—from data issues to training failures to evaluation anomalies.</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:18pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:18pt;white-space:pre-wrap;\">Bonus Points For</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Experience with PyTorch, CUDA, and common CV/3D libraries.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Experience with multi-sensor fusion, BEV architectures, or 3D perception.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Familiarity with MCAP, ROS2, Foxglove, and real-time robotics systems.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Experience with autonomous vehicle pipelines or industrial/agricultural robotics.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Background in active learning or automated label-quality scoring.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Experience building synthetic data augmentations or simulator-driven dataset expansion.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Experience building auto-labeling pipelines</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:18pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Bonsai Robotics is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status or any other category protected by law.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:18pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:18pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>",
      "company": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:18pt;white-space:pre-wrap;\">About Bonsai Robotics</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(29,28,29);font-size:11pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>"
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