Senior Context Fusion AI Engineer - Autonomous Vehicles
for section headings.
We need - for any list of duties/requirements/benefits.
We need
for prose.
We must preserve all substantive information, not invent or summarize away detail.
We must remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The posting includes EEO statement, AI tools, etc. We must remove those.
Also remove duplicated lines: there is duplicate of "Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference." appears both in Requirements and Ways To Stand Out. We need to preserve all substantive information; but if duplicated, we should not repeat? The instruction: "remove duplicated lines". So we should deduplicate.
We need to preserve all substantive information, but we can deduplicate identical lines.
Let's extract content.
Intro paragraph (About the role): We'll keep as is.
Responsibilities: "What You'll Be Doing" bullet list:
- Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation.
- Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty.
- Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions;
- Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention.
- Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior.
- Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models.
- Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems.
- Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in closed-loop simulation and on-road evaluation.
Note there are some stray semicolons and punctuation; we keep as is.
Requirements: "What We Need To See" bullet list:
- BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field, or equivalent experience.
- 8+ years of experience, with at least 2+ years in the AV or robotics industry and 2+ years of leadership experience in a technically area (typo: "technically area" maybe "technical area").
- Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems.
- Demonstrated experience with learning-based multimodal perception or fusion involving two or more cameras, LiDAR, radar, map, navigation, and ego-motion signals.
- Solid understanding of 3D geometry, coordinate frames, calibration, temporal synchronization, ego-motion compensation, tracking, uncertainty estimation, and sensor failure modes.
- Experience with deep-learning methods for 3D perception, lego-context scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning.
- Strong C++ and Python programming skills, with hands-on experience developing, training, and optimizing deep-learning models in PyTorch. Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference using NVIDIA software and hardware is highly valued.
- Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference.
- Experience training and evaluating models at scale, including distributed training, dataset curation, offline evaluation, simulation-based validation, and production monitoring.
- Ability to work across research and engineering boundaries: turn an ambiguous AV problem into measurable technical objectives, build the solution, and drive it to deployment.
Note: there is a typo "technically area". We'll keep as is.
Ways To Stand Out From The Crowd (Preferred Qualifications/Skills):
- Experience building multi-task driving models that jointly predict perception, road structure, occupancy, motion, and/or trajectories from shared multimodal features.
- Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference. (duplicate)
- Experience with BEV, point-cloud/voxel, neural scene representation, 3D reconstruction, occupancy-flow, or spatiotemporal world-model methods.
- Publications or open-source contributions in computer vision, robotics, machine learning, 3D perception, multimodal learning, or autonomous driving.
- Experience optimizing models for automotive-grade real-time deployment using NVIDIA GPUs, TensorRT, CUDA, or edge inference toolchains.
We need to deduplicate the duplicate line about Transformer etc. Keep only one occurrence.
Pay: "Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits."
We could split into Pay and Benefits. Pay: salary range. Benefits: equity and benefits eligibility.
Thus:
Pay section:
Pay
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
Benefits section: Benefits
You will also be eligible for equity and benefits.
Schedule: none.
We must not include sections with filler like "commensurate with experience". Not present.
Now produce HTML fragment.
We need to ensure we don't include any markdown fences.
Let's craft:
About the role
We are looking for a strong engineer to join the DRIVE Road Structure / Online Mapping / Context Fusion team. In this role, you will help craft and guide the future of our L3/L4 autonomous-driving solution by building a complete, learned 3D/4D world model that fuses navigation, ego-motion, perception, and sensor signals. You will work closely with perception, prediction, planning, and simulation teams to deliver a world representation that is complete, temporally consistent, uncertainty-aware, and robust enough to drive through the most challenging roads and intersections in L3/L4 autonomy level. This role is central to our vision for AV: developing a shared multimodal scene representation that can jointly support road understanding, 3D object and occupancy perception, motion prediction, and route-conditioned planning. Are you interested in inventing human-level AI for navigation in the unconstrained world under any conditions? If so, join us!
Responsibilities
- Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation.
- Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty.
- Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions;
- Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention.
- Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior.
- Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models.
- Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems.
- Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in
for prose. We must preserve all substantive information, not invent or summarize away detail. We must remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The posting includes EEO statement, AI tools, etc. We must remove those. Also remove duplicated lines: there is duplicate of "Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference." appears both in Requirements and Ways To Stand Out. We need to preserve all substantive information; but if duplicated, we should not repeat? The instruction: "remove duplicated lines". So we should deduplicate. We need to preserve all substantive information, but we can deduplicate identical lines. Let's extract content. Intro paragraph (About the role): We'll keep as is. Responsibilities: "What You'll Be Doing" bullet list: - Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation. - Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty. - Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions; - Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention. - Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior. - Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models. - Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems. - Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in closed-loop simulation and on-road evaluation. Note there are some stray semicolons and punctuation; we keep as is. Requirements: "What We Need To See" bullet list: - BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field, or equivalent experience. - 8+ years of experience, with at least 2+ years in the AV or robotics industry and 2+ years of leadership experience in a technically area (typo: "technically area" maybe "technical area"). - Strong experience developing production-quality sensor-fusion, perception, state-estimation, or autonomous-driving systems. - Demonstrated experience with learning-based multimodal perception or fusion involving two or more cameras, LiDAR, radar, map, navigation, and ego-motion signals. - Solid understanding of 3D geometry, coordinate frames, calibration, temporal synchronization, ego-motion compensation, tracking, uncertainty estimation, and sensor failure modes. - Experience with deep-learning methods for 3D perception, lego-context scene representation, occupancy/occlusion prediction, semantic segmentation, object detection/tracking, motion prediction, or planning. - Strong C++ and Python programming skills, with hands-on experience developing, training, and optimizing deep-learning models in PyTorch. Experience with CUDA, distributed training, mixed-precision techniques, and efficient GPU inference using NVIDIA software and hardware is highly valued. - Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference. - Experience training and evaluating models at scale, including distributed training, dataset curation, offline evaluation, simulation-based validation, and production monitoring. - Ability to work across research and engineering boundaries: turn an ambiguous AV problem into measurable technical objectives, build the solution, and drive it to deployment. Note: there is a typo "technically area". We'll keep as is. Ways To Stand Out From The Crowd (Preferred Qualifications/Skills): - Experience building multi-task driving models that jointly predict perception, road structure, occupancy, motion, and/or trajectories from shared multimodal features. - Experience with Transformer, VLM, or multimodal foundation-model architectures, including pre-training, fine-tuning, distillation, quantization, or efficient inference. (duplicate) - Experience with BEV, point-cloud/voxel, neural scene representation, 3D reconstruction, occupancy-flow, or spatiotemporal world-model methods. - Publications or open-source contributions in computer vision, robotics, machine learning, 3D perception, multimodal learning, or autonomous driving. - Experience optimizing models for automotive-grade real-time deployment using NVIDIA GPUs, TensorRT, CUDA, or edge inference toolchains. We need to deduplicate the duplicate line about Transformer etc. Keep only one occurrence. Pay: "Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits." We could split into Pay and Benefits. Pay: salary range. Benefits: equity and benefits eligibility. Thus: Pay section:
Pay
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
Benefits section:Benefits
You will also be eligible for equity and benefits.
Schedule: none. We must not include sections with filler like "commensurate with experience". Not present. Now produce HTML fragment. We need to ensure we don't include any markdown fences. Let's craft:About the role
We are looking for a strong engineer to join the DRIVE Road Structure / Online Mapping / Context Fusion team. In this role, you will help craft and guide the future of our L3/L4 autonomous-driving solution by building a complete, learned 3D/4D world model that fuses navigation, ego-motion, perception, and sensor signals. You will work closely with perception, prediction, planning, and simulation teams to deliver a world representation that is complete, temporally consistent, uncertainty-aware, and robust enough to drive through the most challenging roads and intersections in L3/L4 autonomy level. This role is central to our vision for AV: developing a shared multimodal scene representation that can jointly support road understanding, 3D object and occupancy perception, motion prediction, and route-conditioned planning. Are you interested in inventing human-level AI for navigation in the unconstrained world under any conditions? If so, join us!
Responsibilities
- Design and develop learning-based, multimodal sensor-fusion systems that transform synchronized sensor history, ego-motion, navigation context, and driving context into a unified spatiotemporal world representation.
- Build architectures that jointly reason over camera, LiDAR, radar, and vehicle-state inputs, with appropriate handling of calibration, synchronization, coordinate transforms, sensor latency, and uncertainty.
- Develop end-to-end and multi-task models that produce driving-relevant outputs from a shared scene representation, including; road graph elements such as lanes, boundaries, crosswalks, and traffic controls; semantic scene understanding; occupancy and free-space representations, including uncertain and occluded regions;
- Develop scalable multimodal fusion architectures, including Transformer-based early, late, and hierarchical fusion; BEV, point/voxel, and image-based representations; temporal context aggregation; and cross-modal attention.
- Create training, fine-tuning, and evaluation pipelines for large-scale multimodal datasets. Define multi-task objectives and metrics that balance perception quality, geometric consistency, prediction accuracy, latency, and safety-critical behavior.
- Investigate foundation-model approaches for autonomous driving, including vision-language models, multimodal pre-training, representation learning, and efficient deployment of learned world models.
- Work closely with perception, mapping, prediction, planning, simulation, data, and embedded-software teams to convert research advances into robust, production-quality AV systems.
- Develop systematic analysis and debugging tools for model failures, cross-sensor disagreement, long-tail scenarios, distribution shift, and regressions in