דרושים » תוכנה » Expert Deep Learning Research Engineer - Road Detection

Required Deep Learning Algorithm Engineer - REM Semantics
You will be part of our REM (Road Experience Management) department which is responsible for the automatic High Definition map-making process, which is a key technology in our autonomous driving and high-end Driving Assistance systems.
You will be joining the team that builds the state-of-the-art AI-powered relevancy systems at scale. You will be designing and implementing semantic understanding engines that power data-driven products across autonomous driving and beyond, combining cutting-edge Computer Vision, Graph Neural Networks, and efficient inference to solve real-world perception challenges.
If you're talented, highly motivated and passionate about building intelligent systems where your contributions drive concrete, measurable outcomes, we'd love to meet you.
What will your job look like:
Conduct hands-on research and development of AI and Deep Learning algorithms for real-world automotive perception tasks
Own projects end-to-end - from concept, through research, prototyping, designing, training, and evaluating neural network architectures, to integration and scalable deployment
Analyze challenging edge cases and continuously improve model performance in complex driving scenarios
Collaborate with algorithm developers, software engineers, product managers and system teams across the organization
Work with large-scale datasets and real-world driving scenarios to improve model accuracy, robustness, and runtime efficiency
Bring your ideas into production-ready systems that have a direct, measurable impact on autonomous vehicle technologies used worldwide

We are looking for an experienced Deep Learning Algorithm Developer to join one of our teams developing perception technologies for autonomous driving.
Our group tackles diverse challenges - from object detection and scene understanding to 3D perception - using large-scale, real-world in-house datasets and state-of-the-art deep learning models.
What will your job look like:
Develop and train neural networks for complex perception tasks in real-world driving environments.
Design and analyze deep learning pipelines, from data to deployment.
Collaborate with algorithm, software, and data experts to deliver production-ready models.
Research and apply new methods in computer vision, 3D perception, and multi-task learning to improve performance and efficiency.

Required Algorithm Researcher - Autonomous Driving (CTO Group)
The CTO Group is a small, elite team focused on the decision-making module of our autonomous driving system.
Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning.
The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data.
The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction.
What will your job look like:
Research and develop innovative algorithms for autonomous driving planning and decision-making.
Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems.
Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies.
Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance.
Design and implement high-quality production-ready code in a research-driven environment.
Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning.

We are looking for a Deep Learning Tech Lead for Objects Modeling, who will be challenged by solving real-world problems using Computer Vision. Our mission in the algorithms department has a significant impact on autonomous vehicle technologies, external customers, and advanced driver-assistance systems (ADAS). In this role, you will be responsible for the full cycle of deep learning R&D, including modeling new solutions and enhancing the performance of existing ones. Your responsibility will include design, implementation, guidance and mentoring for team members. You will leverage your extensive experience to lead multiple deep projects, and will be a key figure in leading successful execution and in finding creative solutions to difficult bottlenecks, across data, AI, engineering and product considerations.
This role offers the flexibility to work part-time from our other sites, subject to seat availability.
What will your job look like:
Lead the design, development, and implementation of accurate and robust AI algorithms for new projects
Research the latest advancements in deep learning, multi-task learning, AI architectures, and apply them to enhance our algorithm
Design, develop and implement deep learning algorithms to solve complex problems in computer vision
Take ownership of deep learning projects: Design solutions, oversee execution, and collaborate with talented Algorithm developers
Collaborate with cross-functional teams to understand requirements and develop solutions.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

we are looking for a Research Scientist .
As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.
What you will be doing:
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.

We are looking for an MLOps Software developer to help us design and implement our large-scale platform using the most up-to-date technologies.
What will your job look like:
Collaborate with algorithm developers to research, develop, and deploy machine learning models at a large scale.
Leverage our data sources and cloud computing to build an infrastructure that process terabytes of data to improve our machine learning models.
Perform software research, development, evaluation, and optimization.
Design and implement large scale systems.