CVPR 2026 Career Opportunities

Foster City, CA

The Perception team at Zoox creates the "eyes and ears" of our self-driving robots. Navigating safely and efficiently in complex environments requires detecting, classifying, tracking, and understanding various attributes of surrounding objects—all in real-time and with exceptional accuracy.

As an engineer in the Scene Understanding team, you will develop advanced Vision-Language-Action (VLA) models that perceive our vehicle's surroundings to identify hazards and make driving suggestions. You will utilize VLA models for detecting rare events and ensuring safe driving in these situations. You'll work with state-of-the-art machine learning models that operate in real-time on our robotaxi platform with minimal latency. Collaborating with world-class engineers and researchers across sensors, planning, and other teams, you'll have access to premium sensor data and cutting-edge infrastructure to validate your algorithms in real-world conditions. In this role, you will... Design and train Vision-Language-Action (VLA) solutions for robotaxis

Lead end-to-end data strategy, including mining, auto-labeling, and dataset construction to power our ML flywheel

Lead the full post-training stack for VLMs and VLAs, including Continual Pre-training (CPT) on domain-specific driving data, Supervised Fine-Tuning (SFT) for instruction following.

Utilize our large-scale data pipelines and ML infrastructure to research, prototype, and deploy solutions that improve driving behavior

Partner with cross-functional teams to integrate perception signals

Qualifications MS or PhD in Computer Science or related field

Background in deep learning solutions for VLM and VLA models

Track record in post-training large-scale models, CPT, SFT, RL

Hands-on experience with production ML pipelines, including dataset creation, training frameworks, and metrics

Expertise in Python libraries (PyTorch, NumPy, Pandas, VLLM)

Bonus Qualifications Deep knowledge of cutting-edge computer vision techniques

Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)

Experience with integrating large language models to various tasks.

$189,000 - $290,000 a year

Foster City, CA

Do you enjoy applying machine learning to complex, real-world problems in autonomous vehicle testing? The Simulation Scenario Generation team is looking for a ML Engineer to enable next-generation scalable AV scenario creation workflows. This ranges from generating large-scale traffic simulations to extending our agentic AI system to assist in synthetic scenario creation from a natural language test specification. This role offers a unique chance to deliver immediate user impact while contributing to long-term AI-driven safety validation. In this role, you will: Contribute to tooling for AI-based scenario understanding and validation. Synthesize realistic AV simulation scenarios with dynamic (e.g., traffic) and static features. Integrate and validate LLMs/VLMs and implement other models for complex scenario generation workflows, leveraging techniques like agentic tool use. Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows. Directly contribute to the safety and reliability of Zoox's autonomous software. Qualifications MS or PhD in Computer Science, Machine Learning, or related field 5+ years of industry experience in Machine Learning Proficiency in Python and ML libraries (PyTorch, JAX, NumPy, etc.) demonstrated through professional or research projects Demonstrated experience in transformer and diffusion architectures Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems Bonus Qualifications Familiarity with autonomous vehicles, robotics, and/or complex simulation environments Hands-on experience in areas like program synthesis and/or formal methods/V&V Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA) $233,000 - $290,000 a year Base Salary Range

Senior, ML Engineer - Offline Perception

Job ID: 102399
Location: [Ann Arbor, MI , Montreal, Canada, Remote - Canada, Remote - US]
Apply: View and apply through Torc Careers

Meet the Team

The Pseudo-Labeling team’s goal is to create high-quality annotations on sensor data, including images and point clouds. These annotations include 2D and 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, and more.

The annotations are used by downstream teams across Torc. For example, perception teams use them to train various models, and simulation teams use them to generate new data.

What You’ll Do

• Design, implement, test, and deploy offline object detection, tracking, and fusion modules to automatically create annotations on cloud services from logged sensor data, including cameras, LiDARs, and radars.
• Demonstrate project management skills by serving as a project lead and guiding less-experienced team members across multiple facets of project execution.
• Stay up to date with the latest developments in AI and ML for autonomous driving.
• Independently develop offline perception models or algorithms using disciplined software development processes.
• Make recommendations for developing new code or reusing existing code, implementing version control, and maintaining documentation of created applications.
• Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.
• Proactively assess current capabilities to identify areas for improvement and propose solutions that align with core strategy and operations.
• Measure and track auto-labeling quality to meet internal customer requirements.
• Guide and produce information products that support visualization and data accessibility in a customer-centric manner.
• Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.
• Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.
• Provide technical guidance, business process expertise, technical leadership, coaching, and mentoring to team members.

What You’ll Need to Succeed

• Considered highly skilled and proficient in discipline, with the ability to conduct complex, important work under minimal supervision and with wide latitude for independent judgment.
• Expected to drive alignment across team interfaces and the broader organization.
• Ability to design, maintain, and own team technical solutions while driving consensus.
• Experience mentoring and guiding engineers within the group.
• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, or a related technical field, plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience; or
• Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related technical field, plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience.

Required Skills

Some combination of the following skills:

• Active learning and pseudo-labeling experience, including computer vision, deep learning, and model training.
• Experience with at least two of the following: 2D/3D object detection, tracking, sensor fusion, semantic segmentation, SLAM, or BEV.
• Scaled ML operations and tooling experience, including ML frameworks, experiment tracking, model registry, MLflow, Weights & Biases, ML metrics, evaluation, and quality.
• Experience with distributed machine learning frameworks such as PyTorch, Lightning, or Ray.
• Model data curation experience, including Parquet data processing with PyArrow, Daft, Pandas, or similar tools.
• Proficiency in Python software development at scale.
• Experience with VDI

US Pay Range: $199,200 - $298,800 USD

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models.

We are seeking an exceptional Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases.

Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Drive technical direction for specific research initiatives, ensuring robust performance in production environments.

A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Collaborate with fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Collaborate with fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Contribute to focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Make significant hands-on contribution to technical solutions

About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance.

Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

Junior Computer Vision & Machine Learning for Autonomous Anti-Drone Systems

Company Overview:

Allen Control Systems (ACS) is a cutting-edge defense startup, founded by two ex-Navy electrical engineers with a proven track record in robotics and software. We are developing a small, autonomous gun turret that employs advanced computer vision and control systems to precisely target and neutralize small drones and loitering munitions. Our innovative approach requires overcoming significant technical challenges, making this an exciting and dynamic environment for experienced engineers.

With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.

What You'll Do:

Development and optimization of computer vision algorithms for our autonomous gun turret, focusing on real-time drone detection, tracking, and classification.

Design and implement machine learning models that can operate in resource-constrained environments while maintaining high accuracy and reliability.

Collaborate closely with electrical engineers to integrate computer vision systems into the turret's hardware architecture.

Conduct extensive testing and validation of computer vision algorithms in various scenarios to ensure robustness and performance under different environmental conditions.

Contribute to the hardening of the prototype turret into a military-grade system, and assist in developing variants for different weapon systems and engagement ranges.

What You'll Need:

Deep passion for machine learning, computer vision, and robotics, and have been exploring these areas since early in your career.

At least a Bachelor's degree in Computer Science, Electrical Engineering, or a related field, with a strong focus on machine learning and computer vision.

0-3+ years of experience working on machine-learning-based computer vision, ideally in the context of robotics.

A proven track record of developing and deploying computer vision systems, ideally in real-time or safety-critical applications.

Proficient in Python, C++, and have experience with machine learning frameworks such as TensorFlow, PyTorch, or similar.

Experience with embedded systems and integrating computer vision algorithms into hardware.

Familiar with various sensors (e.g., cameras, LIDAR, RADAR) and their integration into autonomous systems.

You enjoy collaborating with other engineers to solve complex technical challenges.

What We Offer:

Competitive salary

ACS Equity Package

Health, Dental, Vision Insurance

Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

United States, California, Santa Clara

Want to join a fun, creative company that is on the cutting edge of outstanding technologies? NVIDIA is developing groundbreaking solutions in some of the most exciting technology areas globally, including Virtual Reality, Artificial Intelligence, Deep Learning and Autonomous Vehicles.

NVIDIA's AV Eval team is building the next generation of driving behavior evaluation — moving beyond hand-crafted rules to learned evaluation using LLMs, VLMs, and agentic workflows. You'll define how we measure whether an autonomous vehicle drives well, building systems that bridge ML research and production evaluation. You'll ship systems that run at scale on real-world driving data and produce metrics that block or green-light software releases. In this role you will get to work on next-gen AV evaluation and create a direct impact on vehicle safety and shipping decisions. Join a new team being built from scratch — high ownership, high visibility to NVIDIA AV leadership.

What You will be doing: - Design and build learned evaluation pipelines that assess driving behavior using LLMs, VLMs, and multimodal models - Develop agentic workflows that chain model inference, retrieval, and structured reasoning to evaluate complex driving scenarios - Define evaluation-of-evaluation methodology — how do we know our learned evaluators are correct? - Build golden-set frameworks and calibration loops for learned metrics - Partner with AML (Alpamayo Logos) teams on model-specific eval needs (e.g., COT prediction quality, AML regression coverage) - Instrument evaluation systems with robust experiment tracking, A/B comparison tooling, and model versioning - Contribute to the team's transition from rule-based to learned evaluation: identify metrics and analyzers that are candidates for ML replacement and build the alternatives

What we need to see: - PhD (4+ years), MS (6+ years), or BS (or similar) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field. - Hands-on experience building LLM/VLM-based pipelines — fine-tuning, prompt engineering, retrieval-augmented generation, chain-of-thought - Track record of shipping ML systems to production (not just prototyping or publishing) - Strong software engineering fundamentals — writing clean, tested, reviewable code in Python and C++ - Experience with evaluation methodology: precision/recall, inter-rater reliability, calibration, annotation pipelines - Comfort with large-scale data processing (Spark, Dask, or similar) - Strong Python skills. Experience with PyTorch or JAX. Comfortable with GPU-based training workflows.

Ways to stand out: - Autonomous driving, robotics, or safety-critical domain experience - Familiarity with driving behavior taxonomies (cut-ins, hard braking events, lane-keeping metrics, scenario-based evaluation) - Experience with video understanding models or multi-modal evaluation. Knowledge of agentic AI frameworks (LangChain, DSPy, CrewAI, or custom) - Track record of influencing technical direction across team boundaries - Experience with LLM/VLM fine-tuning or application development

At NVIDIA, we’re dedicated to making self-driving vehicles a reality and believe this technology can save millions of lives. Join a team of innovative thinkers at one of the world’s most respected technology companies. If you’re motivated, curious, and ready to make a difference, we’d love to meet you! We believe that building self-driving vehicles will be a defining contribution of our generation (e.g. traffic accidents are responsible for ~1.25 million deaths per year world-wide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world’s most desirable employers with some of the most forward-thinking people in the world working here. If you're entrepreneurial and autonomous, we want to hear from you!

Location: London or Amsterdam

We're hiring a few people, and are looking for research-minded engineers to build real-world AI for safety- and privacy-critical settings, specifically policing. For police to serve people in a transparent and consensus-based way is demanding for many reasons. Lack of good interactive tools should not be among their challenges. Why are we still in stealth-mode and who are "we"? I'm Gabriel Brostow and you can talk to anyone in my network to learn more about me. I joined because the founding team is awesome. We'll lift the veil on the company once we ship, and everyone can see and scrutinize that we're indeed firmly on the conscientious end of AI. Interviewees will be asked to sign NDA's but obviously get to see it up close.

The roles span from cloud training infrastructure to inference on low-power edge devices. We want people who think like scientists: you form hypotheses, design experiments, build or stress-test benchmarks, and make progress while mitigating for uncertainty.

Several roles are open, and for strong candidates with range, the lines between them are negotiable: • Vision-language models - data preparation, architecting, training, and applied research. • Vision model distillation & edge deployment - training and shipping models for low-power devices. • Cloud engineering for ML - training and inference infrastructure at scale.

A computer-vision background is one natural fit. But we're working on multimodal models, so your ability to absorb and apply new findings might show up on your CV in other ways.

Published, shipped, or both? If you like hard engineering problems end-to-end, let's talk. (Team is partly in central London and partly in Amsterdam, so it's ideal if you're local or open to relocating)

If you're excited by this but not sure you fit, please reach out anyway. We'd rather hear from you than have you count yourself out.

Location: Beijing

Responsibilities 1. Foundation Models ①Build a unified foundation model for autonomous driving; develop multimodal backbone architectures and sub‑tasks; apply model distillation and lightweighting. ②Leverage language alignment, generative self‑supervision, semi‑supervision, and other techniques to advance large‑scale pre‑training, improving generalization and robustness in complex scenarios. 2. Data & Model Iteration ①Build automated data pipelines using 3D geometry, reconstruction, and related technologies. ②Design and implement a multi‑stage pre‑training to post‑training workflow, along with data utilization strategies.

Requirements 1. Full‑time Master’s degree or above in Computer Science, Electronic Engineering, Automation, Vehicle Engineering, or a related field, with research focused on computer vision, deep learning, robotics, or equivalent areas. 2. Familiar with mainstream deep learning frameworks; proficient in the end‑to‑end workflow of model training, fine‑tuning, and deployment. 3. Experience in developing foundation models (e.g., Transformer, multimodal fusion models); familiar with distributed training, mixed‑precision acceleration, and related techniques. 4. Solid programming skills with strong proficiency in Python/C++; ability to perform high‑performance code optimization and develop complex systems. 5. Strong passion for autonomous driving technology, with excellent learning ability and technical insight. 6. First‑author publications in top‑tier conferences or journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML) is a strong plus. 7. Hands‑on experience in autonomous driving, robotics, or large‑scale AI model projects is preferred. 8. Familiarity with large‑scale data processing tools; experience in large‑scale distributed model training (1,000+ cards/nodes) is highly preferred.

Foster City, CA

We’re reinventing personal transportation—making the future safer, cleaner, and more enjoyable. We’ve created our vehicle specifically for dense, complicated urban environments. Zoox is the only driving vehicle on the market with bidirectional driving capabilities and four-wheel steering, allowing us to maneuver through compact spaces and change directions without the need to reverse. The future is for riders!

Our growing Perception team is searching for an Engineering Manager for the Perception Occupancy and Rare Events team. Our perception stack is responsible for building Zoox’s world-class 3D environment model from multi-modal sensor data. We create novel AI architectures that combine the latest academic results with many in-house innovations in creative ways. Our algorithms are relentlessly optimized and tuned to run efficiently and effectively on a wide variety of real-life data as well as in simulation.

You will be responsible for leading high-impact Perception teams with their technical roadmap and milestone goals. You will be closely collaborating with other AI teams including Simulation, Sensor and Hardware, and Systems Design and Mission Assurance teams to deliver an exceptional objects occupancy perception system encompassing modalities such as vision, lidar, radar and long-wave IR. You will lead a diverse, experienced team with a rapidly growing scope and responsibility while also working on some of the most complex problems in artificial intelligence, perception, and sensor fusion. In this role, you will: Lead Technical Strategy: Build and lead a team of managers and engineers responsible for core occupancy modeling and rare event detection, driving the team’s roadmap, productivity, and execution. Drive Model Evolution: Own the development and deployment of critical perception models, overseeing the consolidation of multi-modal architectures and guiding technical design to enhance detection range, robustness and accuracy. Innovate with Advanced ML: Lead the adoption of state-of-the-art computer vision and machine learning techniques—such as foundation models and efficient geometrics representations—to improve performance on long-tail classes and support geofence expansion and fleet size expansion. Collaborate on Simulation & Data: Collaborate cross-functionally with simulation and data teams to leverage synthetic data and edge-case scenarios, ensuring the perception system performs reliably in adverse weather and safety-critical environments. Ensure Organizational Excellence: Drive organizational health by balancing technical leadership with people management, establishing best practices for data-driven decision-making, and enabling the team to scale effectively. Qualifications: Strong understanding of computer vision systems, AI software stacks, and sensor fusion across multiple modalities. 3+ years of technical leadership experience, 10+ years of experience in computer vision, machine learning and related fields. Strong leadership skills in recruiting, leading, growing, and managing technical team members in solving challenging problems, and building critical components of a real-time system Expertise in implementing autonomy solutions and deploying real-world systems Experience with recent AI approaches including VLM, LLM, Transformers and GenAI (3D/4D GS, Diffusion, NeRF). Bonus Qualifications: Familiarity with perception of autonomous vehicles or similar robots Hands-on experience having deployed real products or platforms into the real world, and intimately understanding the challenges of working with complex systems Involvement in validation or evaluation of risk and/or safety-critical systems $277,000 - $333,000 a year

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models.

We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence.

We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment.

Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration.

As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments.

Key job responsibilities - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.