דרושים » תוכנה » Applied scientist, Agentic AI, Agentic AI

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.

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.

We are seeking an Applied Scientist to help build Amazons next-generation customer memory and personalization systems.

As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems.

You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences.
You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage.
You will collaborate closely with engineering and product teams to translate research into measurable customer impact.

We're looking for a Senior AI Infrastructure Engineer to join a group that specializes in Security and Networking, and specifically ML/AI, MLOps, and agentic AI development. As a Senior AI Infrastructure Engineer, youll build and maintain the infrastructure, tools and processes necessary to support the AI lifecycle in a production environment. You will collaborate closely with data scientists, software engineers, and security architects to ensure smooth development, deployment, evaluation, and optimization of AI pipelines, models, and agents. This role requires a balance of high-level engineering rigor and a collaborative spirit; youll be a technical anchor and a supportive peer for teams across the organization.



What youll be doing:

Architecting, developing and optimizing scalable infrastructure for deploying security and networking AI models and agents in production.

Managing ML/agentic workflows to ensure performance, high availability, resource efficiency, and cost-effectiveness.

Designing and implementing pipelines and frameworks for AI training, inference, and experimentation.

Partnering with data scientists and security architects to operationalize AI agents, including packaging and integration with existing systems. This includes contributing to and reviewing code, design documents, and test plans.

Partnering with DevOps teams to integrate pipelines and workflows into CI/CD processes, ensuring reliable deployments and rollbacks.

Building proactive monitoring systems to identify issues in quality and infrastructure before they impact production.

Implementing access controls, authentication mechanisms, and encryption standards to keep our AI models and data secure.

Documenting guidelines and leading knowledge-sharing sessions to elevate the teams collective development expertise.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers.
You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

As our AI Applied Researcher, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore, research, and develop cutting-edge solutions that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team, you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the technologies in the world, witnessing firsthand their impact on our customers.

A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Research, design, and build anomaly classification, predictive, insights, recommendations, and other types of models from various data sources, including sensor time-series data, textual data, images, and more.
You will Ensemble multi-modalities by exploiting and enriching the most from our data collections platform and feature extraction and store phases, using classic statistical methods, deep learning, Transformers, Graphs, Foundations Modules (as TSFM, Graph Transformer, TGNN), and any available technology to extract recommendations for taking real-world, life-changing actions.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Work with cloud-based big data platforms for training, distributed processing & experiments tracking (e.g., Metaflow, Grafana, Outerbounds, Databricks).
Partnering and contributing to modern technologies: LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.
As part of our hybrid working policy, you will be expected to work from the office at least twice weekly, which includes at least one day in Haifa.

We're looking for a Senior Data Scientist to join the AI cybersecurity team in the Security and Networking Architecture group. As a Senior Data Scientist youll have the opportunity to take an active part in the research and development of our world-class networking and data center security products. This role involves creative problem solving alongside engineering teams, and is key for the continued success of AI networking security.

What youll be doing:

Developing agentic AI systems for security, combining generative models, RAG, and tool-augmented reasoning to automate threat analysis and response workflows.

Optimizing and fine-tuning models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.

Developing, implementing and improving models and algorithms across media types, whether time series, images, text, audio or video.

Leveraging data pipelines to efficiently process and transform large volumes of data for training and inference purposes.

Applying alignment techniques and parameter efficient fine-tuning to improve model performance.

Measuring and benchmarking model and application performance to drive improvements.

Driving the gathering, building, and annotation of domain specific datasets for benchmarking and training.

Collaborating closely with software and hardware engineers on new features and improvements. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.

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.

We are looking for a junior Embedded Software Engineer, to join us in building the next generation of networking products. Youll have the opportunity to work on the technologies that power the worlds largest cloud provider, in a dynamic, open, fast-paced environment. Working for Annapurna Labs is thrilling and a constant learning experience. As an embedded SW development engineer, you will be responsible for developing features for Annapurna Labs next-generation hardware, to enable high network bandwidth and packets-per-seconds (PPS) performance, with consistently low latency. You will work to bring up a broad selection of instance types, optimized for various use cases, to suit each of our customers needs, such as varying combinations of CPU, memory, storage, and networking capabilities. As a SW development engineer, you will play a key role in shaping architecture definitions and SW system designs, and help to resolve complex customer issues. You will continuously evolve technically, while working to monitor AWS cloud health, maintain high quality standards, develop highly-optimized code, and provide exceptional customer satisfaction.

Key job responsibilities
We will be conducting a unique onboarding training program, where you will be studying the following with our technical experts and team leaders:
* Embedded systems basics.
* Annapurna technologies in EC2.
* Cloud compute development.
* Networking 101.

We are rebuilding the way chips get built, and were looking for a senior engineer to help lead that transformation.

This is a full-stack platform role on the team responsible for the development environment behind our silicon. You will help define how agentic orchestration and durable execution become first-class parts of the chip-design workflow.

Were looking for someone who has built complex systems before - across backend, frontend, and infrastructure - and is ready to set the technical direction for what comes next.

What You'll be doing:

Architect and lead full-stack EDA platforms end to end, from data models and backend services to the frontends engineers use every day - while making key technical and build-vs-buy decisions.

Own the infrastructure these platforms run on at scale, including CI/CD, containerization, observability, on-prem compute, and cloud-native Kubernetes environments.

Set engineering standards across the team by leading design reviews, improving reliability and developer experience, and mentoring engineers.

Act as a technical leader across silicon, backend, and design-automation teams, helping turn fragile manual flows into dependable automated systems used by many engineers.