דרושים » AI » AI Researcher - World Model

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment. This role sits at the intersection of applied research and deployment. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions. A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.

Responsibilities:
Own the research-to-deployment cycle for driving models - from literature review and prototyping through to production integration.
Design, implement, and iterate on real-time predictive models, including vision-language-action (VLA) models.
Collaborate on reasoning systems, contributing to VLA models that handle planning across varied horizons.
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms.
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack.
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.

We are seeking a Senior Applied Researcher/ Data Scientist to join our core product organization, where you will build and deploy next-generation AI models powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). The role centers on building and scaling intelligent systems powered by LLMs, advanced ML techniques, and agent-based architectures.
You will join a team within our Cyber R&D organization, leading the companys core product portfolio-while driving AI innovation and setting best practices across the domain. The group focuses on developing next-generation technologies, including training and optimizing LLMs and building advanced multi-agent workflows, while mentoring data scientists across R&D.
As a key technical leader, you will guide the evolution of our AI capabilities-converting cutting-edge ideas into impactful, production-ready systems and shaping our long-term direction.
We are looking for visionary applied researchers eager to take on ambitious challenges and push the boundaries of AI in cybersecurity.
Responsibilities
Design and build advanced AI models and systems that power our core products and address real-world cybersecurity challenges.
Own initiatives end-to-end from identifying customer-driven problems and shaping solutions to experimentation, prototyping, and production deployment.
Drive forward new ideas and research directions, turning emerging concepts into practical, scalable solutions.
Influence the organizations roadmap and contribute to long-term technical strategy across our AI and product landscape.

We are seeking an AI Networking Exploration Architect for our Networking Insights Group to bridge the gap between cutting-edge, hyper-scale AI workloads and the datacenter infrastructure that enables them. You will join a small, focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding and end-to-end systems thinking. Your insights will directly shape our products across the full stack-from applications and software libraries to hardware architecture and physical design.

What You'll Be Doing:

Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.

Translate state-of-the-art research into actionable infrastructure, software, and hardware features in partnership with architecture teams.

Rapidly master new AI domains (LLMs, generative models, multimodal systems) and distill key findings for product teams.

Incorporate your deep knowledge of AI applications into our hardware and software roadmaps.

Conduct independent research by formulating hypotheses about workload behavior and validating them through rigorous analysis.

Drive architectural innovation and network optimization by applying your domain expertise to exploratory analysis of real-world Deep Learning (DL) workloads.

Required Research Scientist - Sovereign AI Research
The Dream Job
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
Responsibilities
Open Research Tracks
We are hiring across the following specializations. We expect depth in at least one area and the intellectual range to collaborate across them.
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Define and execute a research within your track, experiments, quality gates, and upper bounds in rigorous manner.
Collaborate across tracks on system integration and cross-disciplinary research.
Collaborate work with engineering teams until production.
Mentor junior researchers and contribute to a culture of technical excellence.

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI, Computer Vision, and Deep Learning? Join our AGI IMAX Science team and contribute to significant advancements in Computer Vision, Multimodal Understanding, Generative AI, and foundational models.

As part of the AGI IMAX Science team, you'll lead innovative research projects and train large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models that directly impact millions of Amazon and our customers. Leveraging Amazon's vast computing power, you'll work alongside a supportive and diverse group of skilled scientists and engineers, building models and services that make a meaningful difference in the industry.


Key job responsibilities
Lead research initiatives in Computer Vision and Multimodal generative AI, advancing model efficiency, accuracy, and scalability.
Train and fine-tune large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models at scale.
Design, implement, and evaluate deep learning models in a production environment.
Collaborate with cross-functional teams to transfer research outcomes into scalable our services.
Publish in top-tier conferences and journals, keeping Amazon at the forefront of innovation.
Mentor and guide other scientists and engineers, fostering a culture of scientific curiosity and excellence.

As an AI/ML Solution Engineer in the AI-Native Development team, you will design and build AI-powered development pipelines, evaluate ML approaches for code generation and review, and drive the adoption of AI-assisted software development across the organization. You will work at the intersection of machine learning and software engineering - selecting the right models, feedback strategies, and evaluation frameworks to make AI-generated code reliable, high-quality, and trustworthy.

What you'll be doing:

Drive architecture, applied research, and hands-on development by defining and building AI-native software engineering solutions.

Design and build AI-powered development pipelines - from code generation and automated review to feedback loops and evaluation systems.

Evaluate and select ML approaches for specific problems: when to use LLM prompting vs. fine-tuning (QLoRA), classical ML (random forest, linear regression) vs. reinforcement learning, RAG vs. structured extraction.

Architect feedback and evaluation systems that measure and improve AI output quality over time.

Review and refine AI solution architectures - evaluate design decisions, identify weaknesses, propose alternatives with reasoning.

Lead proof-of-concept development to validate new AI/ML approaches for development tooling.

Collaborate with the core team to define risk-based development levels and calibrate AI review depth per level.

Required Applied AI Researcher- Sovereign AI Research
The Dream Job
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
This is a hands-on research role embedded within a team of senior researchers. Day to day includes training models, running benchmarks, synthesize data. The researchers you'll work alongside will challenge you technically and invest in your growth.
Responsibilities:
Open Research Tracks
Familiarity with at least one is expected:
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Train and evaluate models across research tracks, iterating fast while documenting rigorously.
Build and maintain benchmarking pipelines and evaluation suites.
Curate, structure, and preprocess datasets; contribute to synthetic data generation workflows.
Run ablations and controlled experiments to support research hypotheses.
Reproduce and stress-test results from recent literature relevant to the group's work.
Collaborate across tracks and with engineering teams through to production handoff.

At our company, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
We are looking for an experienced Research Scientist, Sensing to push UVeyes inspection beyond todays sensing stack. You will evaluate, prototype, and develop AI models for new sensing modalities such as thermal, hyperspectral, acoustic, radar, structured light, and event cameras, surfacing vehicle defects that todays systems cannot see.
A day in the life and how youll make an impact
* Survey emerging sensing technologies and identify the modalities most likely to expand UVeyes defect coverage.
* Prototype with new hardware, build small-scale datasets from scratch, and train modality-specific models.
* Run controlled experiments that benchmark new modalities against todays inspection stack on shared defect scenarios.
* Develop multi-modal fusion approaches that combine new and existing sensors for stronger detection.
* Recommend which modalities graduate into product and partner with R&D and product on integration.

We are seeking a highly accomplished AI Researcher to be part of the development of next-generation Foundation Models and Generative AI systems. In this role, you will bridge the gap between cutting-edge academic research and production-grade AI infrastructure. You will build, design, and fine-tune large-scale architectures (including LLMs, Multimodal, and Tabular Foundation Models) while applying them to complex, real-world domains like agentic behavior, coding and decision making.
In your day-to-day, you will:
Conduct pioneering research in deep learning and generative AI, specializing in Foundation Models, NLP, alignment, and multimodal learning
Deal with the impact of the models on production traffic, cost quality and latency
Train and scale state-of-the-art architectures using techniques like Post-Training SFT, Knowledge Distillation, LoRA, and sparse/hybrid model architectures
Own the AI lifecycle-from algorithmic prototyping and synthetic data generation to deploying production-grade AI features in fast-paced or big-data environments
Drive research initiatives, author publications/patents, and collaborate with other research teams to integrate intelligent capabilities into core products.

Are you an inventive, curious, and driven Applied Scientist with a strong background in AI, Computer Vision, and Deep Learning? Join our AGI IMAX Science team and contribute to significant advancements in Computer Vision, Multimodal Understanding, Generative AI, and foundational models.

As part of the AGI IMAX Science team, you'll lead innovative research projects and train large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models that directly impact millions of Amazon and AWS customers. Leveraging Amazon's vast computing power, you'll work alongside a supportive and diverse group of skilled scientists and engineers, building models and services that make a meaningful difference in the industry.


Key job responsibilities
Lead research initiatives in Computer Vision and Multimodal generative AI, advancing model efficiency, accuracy, and scalability.
Train and fine-tune large-scale Vision-Language Models (VLMs), diffusion models, and multimodal foundation models at scale.
Design, implement, and evaluate deep learning models in a production environment.
Collaborate with cross-functional teams to transfer research outcomes into scalable AWS services.
Publish in top-tier conferences and journals, keeping Amazon at the forefront of innovation.
Mentor and guide other scientists and engineers, fostering a culture of scientific curiosity and excellence.