דרושים » מדעים מדוייקים » AI Algorithm Engineer - Localization Team

Required Senior Deep Learning Algorithm Developer
Ramat Gan
Full time
A global leader in autonomous vehicle (AV) technology, pioneering advanced driver-assistance systems (ADAS) and self-driving solutions.
Across the company, Senior Deep Learning Algorithm Developers work at the forefront of AV/ADAS innovation - designing, training, and optimizing deep neural networks for tasks such as perception, mapping, and scene understanding. This includes working with large-scale datasets, leveraging frameworks like PyTorch or TensorFlow, and applying state-of-the-art techniques to tackle complex perception and decision-making challenges in real-world driving scenarios.
Senior Deep Learning Algorithm Developers contribute across multiple AI domains, with most opportunities in Computer Vision and additional roles in areas such as NLP/LLMs and 3D Perception (Radar, LiDAR, Fusion). This role serves as a central entry point, enabling us to align your expertise and aspirations with the projects and teams where you can make the greatest impact.
What will your job look like?
Design, develop and implement deep learning algorithms to solve complex problems in computer vision
Take ownership of deep learning projects: Compose product requirements, design solutions, oversee execution, and collaborate with talented Algorithm Developers.
Collaborate with cross-functional teams to understand requirements and develop solutions.
Analyze large datasets to improve algorithm performance and robustness.
Research the latest advancements in deep learning, multi-task learning, AI architectures, and apply them to enhance our algorithms.

We are looking for a talented AI Algorithm Developer to join us in the research and development of our next-generation automatic sports video content engine. Our team operates at the intersection of Computer Vision, NLP, and Audio analysis. We dont just use models; we build multimodal pipelines that understand sports context like a human editor would.
If you are an expert in one of these domains (NLP, CV, or Speech) and are eager to master the others to build state-of-the-art Multimodal AI - we are looking for you!
What youll do:
Research and develop end-to-end real-time solutions for sports content automation using state-of-the-art Deep Learning (LLMs, VLMs, Audio models, and Generative AI).
Collaborate with Computer Vision and Audio experts to fuse different data signals into unified models.
Combine deep language models, Generative AI, and classical ML techniques to create high-quality solutions for demanding product-oriented tasks.
Manage the full R&D cycle: from problem definition and reading papers to implementation, prompt engineering, and productization with high autonomy.
Engage in collaboration with product and success teams to convert business goals into measurable algorithmic objectives.
Write robust, production-grade Python code that scales.

Our CTO Group is looking for experienced Algorithm Researchers and Developers to join our team!
Which department will you join?
The CTO Group is a small, elite group, whose goal is to build next-generation algorithmic solutions powering our self-driving car, crowd-sourced HD maps, and advanced driver-assistance systems.
The group is primarily focused on the decision-making module of our self-driving car. It leverages a wide range of algorithmic approaches, rigorous formal definitions, and mathematical modeling- enriched by insights drawn from large-scale data and real-world driving experience.
The group is comprised of top-notch developers and researchers and is headed by world-class scientists.
This is a unique opportunity for a passionate technical leader to join the CTO Group, work closely with the CTO on next-generation projects, develop POCs using cutting-edge technologies, shape our technology vision and provide thought leadership to our technical community.
What will your job look like:
Research, design and develop innovative algorithmic solutions.
Mathematical modeling and abstraction of real-life problems.
Code design, implementation, and hands-on development.

Our CTO Group is looking for an outstanding Physical-AI Applied Researcher to join our team.
The CTO Group is a small, elite research unit shaping the next generation of algorithmic foundations behind our autonomous driving systems. The group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy.
We are seeking a researcher who thrives at the intersection of machine learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems.
In this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. You will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. The problems are open-ended, scientifically challenging, and deployed at unprecedented scale.
This is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. If youre excited about pushing the boundaries of learning-based decision-making, wed love you to join us and help shape the future of Physical AI.
What will your job look like:
Design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
Advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
Integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
Formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
Lead research directions from conception to full-scale production.
Develop using Python (PyTorch or similar frameworks) as well as C++/GPU/Cuda.

Required Senior ML Data Engineer
Ramat Gan
Full time
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
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).

Required ML Engineering Team Lead
Why us:
Youll work in an awesome environment alongside some of the most innovative people in the industry, using cutting-edge technologies and tools (video editing, Gen AI, data, etc.). You have the opportunity to directly influence the products and tools used by our clients, including sports giants such as the NBA, Bundesliga, LaLiga, ESPN - and thats just the beginning of what we have to offer! Join us and be a part of the best team in tech as we Fuel the Fandom worldwide.
As a ML Engineering Team Lead, you will lead a multidisciplinary team of algorithm and software engineers responsible for designing, building, and operating the next-generation content creation engine. The team focuses on leveraging agentic systems and advanced ML technologies to orchestrate complex, end-to-end production workflows at scale.
In this role, you will translate high-level product and business goals into robust, production-ready solutions, working hands-on across the full lifecycle - from research and system design to deployment, monitoring, and continuous improvement. You will collaborate closely with Product, Platform, Data, and other R&D teams in a fast-paced, agile environment, ensuring that algorithms and software are seamlessly integrated into real-world, high-throughput systems.
You will combine technical leadership, architectural thinking, and people leadership to drive innovation, execution, and delivery, while shaping how intelligent, automated content is created and scaled across our products.
What youll do:
Lead and mentor a multidisciplinary team of algorithm and software engineers, fostering ownership, technical excellence, and professional growth.
Own the delivery of complex, end-to-end systems for automated content creation, from early design and experimentation through production deployment, monitoring, and iteration.
Drive the design and orchestration of agentic systems, working hands-on with the team to integrate ML models, business logic, and software infrastructure across research, engineering, and production at scale.
Translate product and business challenges into technical solutions, defining system architecture, prioritizing initiatives, and guiding execution in an agile environment.
Ensure production readiness and reliability, overseeing deployment, monitoring, performance, and continuous improvement of algorithms and services in a live environment.
Collaborate closely with Product, Platform, Data, and other R&D teams, aligning technical execution with product strategy and business goals in a multidisciplinary setting.
Continuously evolve technically and professionally, encouraging knowledge sharing, joint problem-solving, and awareness of emerging trends in ML, agentic systems, and large-scale software architectures.

Required Transformer-Based Deep Learning Algorithm Developer
Ramat Gan
Full time
Join our Perception Solutions team and take part in building a new, impactful project from scratch, shaping the next generation of autonomous driving technology!
Our team is project-oriented, working on significant computer vision challenges and solving them with cutting-edge neural networks combined with heuristic solutions.
In this role, you will join right at the beginning of an end-to-end development cycle. You will leverage advanced approaches, including Transformer-based architectures, to enable direct vision modeling beyond traditional image-processing pipelines.

Alice is building its internal AI infrastructure layer from the ground up. We have real agents running in production, a growing base of employees using AI in their daily work, and a clear architectural direction. What we don't have yet is a dedicated engineer to own it. You'll be the first. Your job is to close the gap between "working prototype" and "production platform" - owning the foundation that hosts our agents, the pipelines that ship them, and the reliability layer (observability, cost controls, audit trails, evals) that makes it safe to run AI at scale in a trust & safety company. This is an infrastructure-first role with deep AI fluency - not a prompt engineer, not a wrapper-framework operator, not a no-code builder. You should be equally comfortable writing a Terraform module, debugging a Kubernetes pod, and tracing an agent's tool-call chain. We don’t operate with a predefined backlog here; you will be responsible for identifying high-impact needs and bringing them to life. The perfect fit for this role has a track record of deploying agentic systems that have held up under real-world usage, balances a focus on infrastructure with a deep concern for user experience, and recognizes that the primary hurdle in AI integration is rarely the model itself. Responsibilities: Platform & Infrastructure
* Architect, build, and run the AWS/Kubernetes platform that hosts Alice's internal AI agents and tools; drive AWS Well-Architected pillars (operational excellence, security, reliability, performance, cost, sustainability).
* Own Infrastructure-as-Code: Terraform modules, standards, and reviews for Bedrock, agent runtimes, vector DBs, and supporting services. AI Systems
* Design and ship production-grade agents and multi-agent pipelines using the Anthropic Agent SDK, Claude Code, AWS Bedrock, and MCP — not wrapper frameworks.
* Own the full agent lifecycle: scoping ? prototyping ? eval ? deploy ? monitor ? iterate.
* Integrate agentic workflows into internal and product systems via APIs, databases, webhooks, Slack, and email. Reliability, Observability, Cost
* Build first-class observability across apps and infra: OpenTelemetry, Prometheus, plus LLM-specific tracing (Langfuse or equivalent), token/cost metrics, and eval pipelines.
* Define SLOs/SLIs and error budgets for AI services - latency, model fallback chains, eval regression gates, agent success rates. Lead incident readiness, response, and post-mortems.
* Drive FinOps: model routing by cost, cache hit rates, batch vs. realtime tradeoffs, budget alarms, per-team chargeback visibility.
* Implement guardrails: prompt-injection defenses, PII redaction, model allowlists, human-in-the-loop checkpoints, audit trails. Org Impact
* Identify high-leverage workflows across the organization and translate them into scalable agentic automations.
* Partner with R&D, Delivery, security, and external vendors to deliver platform capabilities.

About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact—whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Hybrid:
Yes

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing our companys AI models. You will own and evolve our companys benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our companys benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.