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We are looking for a MLOps Engineer with strong production engineering experience building and operating scalable ML and AI systems.

This is a software-first MLOps platform role focused on production reliability, ML lifecycle management, large-scale training infrastructure, operational AI systems, and reusable platform capabilities.

You will help build and scale the production platform behind our Industrial AI Workforce, enabling teams across the company to develop, evaluate, deploy, and operate ML and AI systems consistently and safely.

A Day In Your Life
Design and evolve production MLOps capabilities across the full ML lifecycle including datasets, features, models, evaluations, deployments, monitoring, retraining, and feedback signals.
Build systems for experiment tracking, artifact management, reproducibility, versioning, lineage, promotion workflows, and production readiness.
Develop reusable platform tooling, golden paths, and engineering standards that improve consistency and delivery velocity across teams.
Build operational infrastructure for LLM and agentic systems including prompts, tools, traces, evaluations, observability, safety boundaries, and production monitoring.
Design evaluation and monitoring frameworks for AI systems including answer quality, latency, grounding, reliability, and operational regressions.
Build and optimize large-scale training pipelines supporting heterogeneous data sources and scalable compute patterns.
Write clean, modular, production-grade Python services and platform libraries.
Drive engineering quality through automated testing, CI/CD, observability, deployment standards, and operational best practices.

You are a Senior AI Software Engineer looking to join a GenAI Infrastructure team and build production-grade GenAI capabilities and the infrastructure behind them. You will be a hands-on engineer focused on AI agents, RAG pipelines, tool-calling workflows, backend services, data access layers, evaluation, observability, and reusable GenAI infrastructure used across product teams. You will not be focused solely on model research or prompt design. Instead, you will be a strong software engineer who can design, build, ship, and operate reliable AI-powered systems in production.

A Day In Your Life
Build reusable infrastructure, SDKs, and internal frameworks for AI-powered product capabilities.
Design and implement AI agents, RAG flows, tool-calling workflows, and LLM orchestration pipelines.
Build production-grade GenAI services, APIs, and backend infrastructure.
Integrate LLM workflows with internal microservices, data platforms, vector search, and event-driven systems.
Design secure data-access patterns that enforce authorization, tenant separation, and user-level scope.
Implement evaluation, tracing, monitoring, and quality-control mechanisms for GenAI systems.
Improve latency, reliability, fallback behavior, cost efficiency, and production readiness.
Work on customer-facing AI experiences, including conversational and proactive agentic product flows.
Collaborate with backend engineers, product managers, data engineers, AI/ML engineers, and domain experts.

As an Algorithm and Applied AI Scientist (DSP), you will serve as the technical lead for an innovative project focused on advanced signal processing and data analytics applications. You will work with unique datasets from various signal-generating sources, researching and developing novel models, and analysing, maturing, and extracting features that have a significant impact on industrial manufacturers globally. In this role, you will be instrumental in defining project objectives, identifying customer value, and pushing the team toward successful outcomes. Joining our AI team offers ongoing professional growth in leadership, algorithm development, research, innovations, and technical data analysis.

A Day In Your Life:
Be the technical lead of an innovative project.
Develop a comprehensive understanding of industrial data by exploring its physical properties and the relationships among different data streams.
Oversee the full algorithm development cycle, spanning from initial problem definition to the deployment of production-ready feature-based AI models, SDLC starting from feature discovery that is being tracked by sensor behavior patterns.
Architect and build diverse predictive and anomaly classification models using sensor time-series and textual information.
You will gain a deep proficiency in the industrial world and the connection between datasets to generate actionable insights.
Utilize classic and cutting-edge technologies, such as Generative AI, transformers, TSFM, TGNN, deep learning, and traditional statistical methods, time-series, and textual data to generate actionable insights.
Partner with the product team and external customers to create data-driven solutions that address real-world business needs.

As an Algorithm and Applied AI Scientist (DSP), you will serve as the technical lead for an innovative project focused on advanced signal processing and data analytics applications. You will work with unique datasets from various signal-generating sources, researching and developing novel models, and analysing, maturing, and extracting features that have a significant impact on industrial manufacturers globally. In this role, you will be instrumental in defining project objectives, identifying customer value, and pushing the team toward successful outcomes. Joining our AI team offers ongoing professional growth in leadership, algorithm development, research, innovations, and technical data analysis.

A Day In Your Life
Be the technical lead of an innovative project.
Develop a comprehensive understanding of industrial data by exploring its physical properties and the relationships among different data streams.
Oversee the full algorithm development cycle, spanning from initial problem definition to the deployment of production-ready feature-based AI models, SDLC starting from feature discovery that is being tracked by sensor behavior patterns.
Architect and build diverse predictive and anomaly classification models using sensor time-series and textual information.
You will gain a deep proficiency in the industrial world and the connection between datasets to generate actionable insights.
Utilize classic and cutting-edge technologies, such as Generative AI, transformers, TSFM, TGNN, deep learning, and traditional statistical methods, time-series, and textual data to generate actionable insights.
Partner with the product team and external customers to create data-driven solutions that address real-world business needs.

You will join the Deployment Platform team, which puts our technology directly in the hands of customers, field technicians, and partners. As an AI-Native mobile engineer, your primary development framework is Flutter (with native iOS and Android used when needed), building the mobile application central to our field deployment. You are also comfortable across the full stack to ensure end-to-end feature delivery. Most importantly, you will shape the next generation of agentic installation experiences that define how industrial AI reaches the factory floor. This is a high-impact role at the intersection of mobile, IoT, and AI.

A Day In Your Life
Design, build, and maintain high-quality, cross-platform mobile applications for iOS and Android using the core Flutter/Dart stack.
Lead the design and shipment of agentic-first workflows for guided installation, leveraging LLMs (Azure OpenAI, Gemini) to assist technicians and automate decision-making in the field.
Implement complex IoT/Hardware integration features, including NFC provisioning, AR field tools, and indoor positioning/navigation.
Contribute to backend and frontend services/APIs to ensure seamless, end-to-end feature delivery.
Architect and maintain a scalable, modular monorepo, ensuring robustness through strong state management (BLoC/RxDart) and comprehensive test coverage.
Collaborate with product and design teams to deliver exceptional UI/UX, including adaptive and RTL-aware layouts.

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.

As our AI Applied Scientist, 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 models 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 world's technologies, 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.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

We are looking for a Senior Network SW Engineer to join the SW group and take a major part in redefining the future of our cloud.
Were searching for engineers with a passion for networking to develop SRD (Scalable Reliable Datagram). SRD is a high-performance, low-latency transport protocol used within Amazon Web Services (AWS) infrastructure to optimize network performance. SRD powers several high-impact, cutting-edge products, including on-demand ML and HPC platforms leveraging EFA, next-generation storage services built on EBS, and the future of AWS network traffic.

As a Senior Network Software Engineer on SRD team, your primary role will be to develop and optimize the implementation of SRD technology across AWS's network infrastructure. Your responsibilities will encompass designing, deploying, and maintaining SRD code-base, ensuring its reliability and scalability to accommodate the demands of various applications. An essential part of your role will involve conducting extensive network simulations to evaluate system performance under different conditions, enabling you to identify potential bottlenecks and inefficiencies. Using these simulations, you will troubleshoot network issues and implement robust solutions, leading to minimal data loss and latency. Leveraging your in-depth understanding of network protocols and AWS infrastructure, you'll refine and improve the SRD system's performance. Furthermore, you will be expected to mentor junior team members, leading projects to advance the SRD capabilities within the AWS environment. Staying updated with the latest industry trends and incorporating them into strategic network service planning.

As a Principal Quality and Reliability Engineer, you will play a crucial role in ensuring Annapurna Labs' semiconductor products meet customer demands. Your responsibilities will include leading all Q&R activities for one of our IC product lines, chart the technical direction, build and lead a team of Q&R engineers, direct work with Annapurna's customers to jointly debug failures and reduce IC related failures, and support RMA analysis.
In this role you will have significant impact on, and be completely exposed to, the semiconductor production flow of all Annapurna Labs products, semiconductor testing methodologies and FAB process yield improvement activities.
This is a fast-paced role where you'll work with thought-leaders in multiple technology areas. You'll have high standards for yourself and everyone you work with, and you'll be constantly looking for ways to improve our products' overall performance, quality, and cost.

We are looking for a Sustaining Lead Engineer to join the team responsible for maintaining and improving the manufacturing health of our cloud server platforms at scale.

As a Sustaining Lead Engineer, you will own the ongoing producibility, yield, and quality of our Graviton server products across high-volume manufacturing sites - driving root-cause analysis, process improvements, and supply continuity for products already in mass production. This is a multidisciplinary role spanning hardware, firmware, test, and manufacturing process domains.

Key job responsibilities
- Support new product introduction (NPI) to volume transition by defining production-readiness criteria and validating manufacturing processes at scale.
- Own manufacturing yield, quality, and reliability metrics for Graviton server platforms in volume production; drive systematic root-cause analysis and corrective actions for field and factory issues.
- Partner with Contract Manufacturers (CMs) to monitor process health, resolve production escapes, and implement continuous improvement initiatives across multiple high-volume lines.
- Lead debug and failure analysis of field returns and fleet-level reliability events - identify systemic failure modes, develop containment actions, and drive permanent fixes back into design and manufacturing processes.
- Partner with supply chain to review, identify, and qualify second-source electrical components to mitigate supply-chain risk and ensure continuity of supply without disrupting production.
- Develop and maintain sustaining test strategies - including failure analysis workflows, screening methods, and data-driven disposition criteria - to protect outgoing quality.
- Analyze manufacturing and field data at scale to identify trends, repeat-offender units, and systemic failure modes; translate findings into actionable design or process changes.
- Willing to travel 4-6 times annually for week-long trips to manufacturer sites.