לוח משרות דרושים NLP/Machine Learning משרה מלאה

Required Software Engineer, ML (Technical Leadership)
The financial risk management (FRM) machine learning principal will be the most senior machine learning engineer and strategist for financial risk at Meta. The principal will enable the risk organization to deliver significant lift over current long range objectives for friction and leakage through the generation of new Machine Learning opportunities for the organization and support of successful delivery of the risk management ML architecture. This person will partner closely with the FRM engineering leader (Director level) and be part of Metas risk management leadership circle.
Software Engineer, ML (Technical Leadership) Responsibilities
Address core business and technical machine learning opportunities: elevate the existing portfolio of machine learning solutions to be state-of-the-art for minimizing Metas financial losses (due to leakage, good revenues loss and friction). Following are a few examples of technical and business problems we aim to address. - Provide a solution for optimizing the risk machine learning model ensemble (covering the entire end-to-end advertiser funnel including detection, decisioning, enforcement and remediation) through optimization of the current model portfolio and individual models. - Minimize the impact of the prolonged financial fraud feedback loop. - Improve models measurement and performance. - Optimize data/label strategy. - Optimize balance between specific targeted model strategy and broad umbrella model strategy to optimize for short and long term benefits
Lead Research and Introduction of Advanced Technologies: - Collaborate with Financial Integrity's senior ML Engineers to lead the research and introduction of deep learning and Large Language Model (LLM) technologies. - Remain current on industry-wide advancements in ML and introduce relevant advancements in Financial Risk Management
Collaborate on Next-Generation ML Architecture: - Work closely with financial harms principals and risk management tech leads to deliver the next-generation ML architecture for Meta's risk management system. - Collaborate with Principal ML engineers from across the company to adopt best industry and Meta practices within the FRM team. - Resolve or mitigate design dilemmas, balancing business and technical trade-offs. - Identify and initiate opportunities for collaboration and impact with other organizations at Meta
Identify and Initiate New Business Opportunities: - Collaborate with Meta FinTech, Central Integrity and Core Ads Growth partnerships to identify and initiate new business opportunities based on third-party capabilities. - Conduct proof of concept for different opportunities and initiate integrations to enhance business performance
Grow Other Senior ML Engineers - Actively invest in the growth of other senior ML engineers through goal-driven formal and informal mentorship. Provide regular feedback to other engineers regarding their technical work.

Required Software Engineer, Machine Learning
We are embarking on the most transformative change to its business and technology in company history, and our Machine Learning Engineers are at the forefront of this evolution. By leading crucial projects and initiatives that have never been done before, you have an opportunity to help us advance the way people connect around the world.
The ideal candidate will have industry experience working on a range of recommendation, classification, and optimisation problems. You will bring the ability to own the whole ML life cycle, define projects and drive excellence across teams. You will work alongside the worlds leading engineers and researchers to solve some of the most exciting and massive social data and prediction problems that exist on the web.
Software Engineer, Machine Learning Responsibilities
Leading projects or small teams of people to help them unblock, advocating for ML excellence
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
Suggest, collect and synthesize requirements and create effective feature roadmaps
Code deliverables in tandem with the engineering team.

Join us at our company, a global fintech leader (NASDAQ; TASE: NYAX) revolutionizing the world of cashless payments, consumer engagement, and business management solutions. With more than 1,200 employees across 12 offices worldwide. At our company, youll be part of a diverse and innovative community where your work makes a real impact and helps shape the future of payments.
Syte is an image recognition solution for retailers in different verticals such as fashion, home, furniture, DIY etc. We offer AI-powered product recommendation and discovery platform. Our solutions include visual search, automated product tagging, advanced personalized recommendations, and more.
We are currently looking for a Senior Machine Learning Engineer to design and implement scalable and integrate machine learning solutions into production. You will work with modern technologies like Node.js, Python, Kafka, MongoDB, and OpenSearch, AirFlow, while driving architectural decisions and CI/CD best practices. This role combines backend engineering excellence with ML deployment expertise.
The Senior Machine Learning Engineer will report directly to the Director of R&D.
Your key responsibilities will include:
Architect and develop MLE services using Python, ensuring high performance, reliability, and scalability.
Collaborate with Data Science teams to productionize ML models (model serving, monitoring, retraining pipelines).
Build and maintain CI/CD pipelines for automated testing, deployment, and monitoring.
Design and implement system architecture for distributed, event-driven systems.
Integrate and optimize Kafka for real-time data streaming and event processing.
Design and manage MongoDB schemas and queries for optimal performance.
Implement and maintain OpenSearch clusters for search and analytics use cases.
Ensure security, observability, and fault tolerance across all services.
Mentor team members and contribute to engineering best practices.

We are looking for an outstanding experienced manager to join our leading AI team. The team is developing cutting-edge AI applications including: Retrieval-Augmented Generation (RAG) systems, Large Language Model (LLM) implementations, large scale AI Agents, MCPs, recommendation engines, classical AI models such as clustering, and more. You will have the opportunity to shape how AI transforms our products and internal processes worldwide. In this role, you will manage a team of AI/ ML engineers, lead the innovation and development the different solutions, and collaborate with cross-company teams. We are looking for an experienced, hard working manager who can collaborate effectively and has a deep technical capabilities in both AI/ML and software development.

What you'll be doing:

Manage a team of 6-8 engineers.

Formalize the team work methodologies, collaboration, etc.

Provide technical leadership, mentorship, and guidance to foster team growth.

Supervise and build AI best in class solutions, including data ingestion, RAG, MCPs, Agents, self service, and more.

Be a part of an experienced team with a great atmosphere.

Collaborate with cross-functional teams to identify and implement AI opportunities.

We're in search of an experienced and skilled Senior Data Engineer to join our growing data team. As part of our data team, you'll be at the forefront of crafting a groundbreaking solution that leverages cutting-edge technology to combat fraud. The ideal candidate will have a strong background in designing and implementing large-scale data solutions, with the potential to grow into a leadership role. This position requires a deep understanding of modern data architectures, cloud technologies, and the ability to drive technical initiatives that align with business objectives.

Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.

Your Arena

Design, develop, and maintain scalable, robust data pipelines and ETL processes
Architect and implement complex data models across various storage solutions
Collaborate with R&D teams, data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality solutions
Ensure data quality, consistency, security, and compliance across all data systems
Play a key role in defining and implementing data strategies that drive business value
Contribute to the continuous improvement of our data architecture and processes
Champion and implement data engineering best practices across the R&D organization, serving as a technical expert and go-to resource for data-related questions and challenges
Participate in and sometimes lead code reviews to maintain high coding standards
Troubleshoot and resolve complex data-related issues in production environments
Evaluate and recommend new technologies and methodologies to improve our data infrastructure

We are looking for a skilled and highly motivated senior software engineer to join and play an integral role on the Runtime team, part of the MLOps group at JFrog ML. This team is responsible for building high-quality, enterprise-grade deployment and serving infrastructure for classic ML models and LLMs - ensuring they are efficiently deployed, monitored, and scaled to handle millions of requests across multiple cloud platforms, including GPU-powered machines. As a core member of the team, youll tackle meaningful and engaging challenges across a diverse technological stack.

We value creative, fast learners and independent thinkers who are proactive, collaborative, and capable of driving tasks from concept to production

As a Senior Software Engineer at JFrog ML you will...
Be an integral part of a highly skilled team working to build the leading MLOps platform in the industry
Maintain and evolve the Runtime teams products, ensuring their reliability and scalability.
Design and develop a complete hosting system that supports various types of inference, analytics, monitoring, distribution, and more - enabling customers to run large-scale real-time, batch, and streaming ML pipelines
Play a key role in shaping our cross-company engineering culture
Conduct high-quality design reviews with a strong emphasis on scalability, maintainability, security, and sound use of design patterns
Write maintainable, well-tested code in multiple programming languages.
Continuously improve the efficiency, scalability, and stability of critical system components

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 at our company. 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.

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 autonomous driving and high-end driving assistance systems. vision-based localization is a key enabler for utilizing and creating the maps. our group is responsible for aligning the map with the world, and reporting detected changes during the drive. our technology is safety-critical, and the code has strict time and memory constraints as it operates in real time. what will your job look like:
develop and optimize computer vision and deep learning algorithms to accelerate data generation and labeling workflows for autonomous driving
apply both classical computer vision techniques and modern deep learning methods to solve large-scale data challenges
collaborate closely with development and annotation teams to enhance automation and ensure high-quality data
own the entire lifecycle from prototyping to scalable deployment within internal pipelines and tools
develop tools to evaluate and analyze algorithm performance, robustness, and operational efficiency

We are looking for an exceptional Senior AI Researcher to join our fast-growing company during a pivotal stage of market expansion. In this role, you'll lead groundbreaking research in SOC automation, develop industry-defining benchmarks, create innovative prototypes that transform into product features, and establish thought leadership through conferences and publications. If you're passionate about revolutionizing security operations through AI and excited about shaping industry standards, we'd love to hear from you!

What Will You Do?
Lead research initiatives that redefine SOC automation capabilities, creating industry benchmarks and evaluation frameworks for AI in security operations
Design and develop functional prototypes that demonstrate breakthrough concepts, transitioning research into product features with engineering teams
Collaborate with security researchers, Product Marketing, and the Head of AI to analyze competitors and align research priorities with commercial objectives
Present findings at premier conferences (BlackHat, RSA) and publish tools/content that advance security automation and establish thought leadership
Mentor engineering teams on advanced AI techniques and their practical applications in security contexts

We are looking for a hands-on Tech Lead to join the Core Platform team within ML. Our engineering teams build the foundational systems behind global artifact storage, replication, and distribution - and increasingly power the next generation of AI/ML operations and governance.Our platform is the backbone for ML workloads: managing model binaries, versioning, and scalable runtime environments for ML and AI applications. This role combines deep distributed systems with modern ML infrastructure challenges such as high-throughput inference, safe model rollouts, and multi-cloud GPU efficiency. You will also help evolve core libraries and developer-facing tools, including logging, observability, and visibility components.
As a senior technical leader, you will influence architecture across squads, lead complex development efforts, and remain heavily hands-on.
As a Tech Lead in Core Platform in you will
Design and evolve components for managing and distributing ML/AI models and artifacts at scale
Extend the platform to support reliable, high-performance inference and training workflows
Lead cross-team technical initiatives and serve as a reference for distributed systems and ML infra design
Write maintainable, high-quality code in performance-critical areas.
Mentor engineers and drive strong engineering practices
Collaborate with adjacent teams to ensure seamless end-to-end ML platform behavior
Improve the reliability, efficiency, and observability of core services

We are seeking a Backend Team Lead to spearhead the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact leadership role at the intersection of backend architecture, multimodal AI, and real-time systems. You will architect and lead the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Lead & Mentor: Build and manage a high-performing backend/AI engineering team, drive architectural decisions, and foster rapid innovation while maintaining production-grade reliability.
Design AI-Native Systems: Architect scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Design distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

We are seeking a Backend Engineer to join the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact role at the intersection of backend architecture, multimodal AI, and real-time systems. You will contribute to the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Design AI-Native Systems: Design and implement scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design and implement multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Contribute to distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

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 - you will go from reading papers to shipping production systems. 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 predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
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 aren't building a single, generic chatbot. We are building a Composable AI Microservice Architecture, a swarm of hundreds of hyper-specialized AI services, each meticulously "programmed" to solve small, focused tasks with high precision. This fleet powers Ava, our AI support engine, and a suite of cutting-edge generative tools for travel and expense management.
As a Senior AI Ops / MLOps Engineer, you are the architect of the platform that makes this scale possible. You will move beyond traditional MLOps to manage a "factory" of Language Models. Your challenge is one of orchestration and standardization, ensuring that every service in the swarm meets a rigorous bar for quality, reliability, and cost-efficiency.
What You'll Do:
Orchestrate the AI Fleet: Build and own the runtime environment for 100+ specialized AI services. Manage model routing, context versioning, and standardized memory/history stores.
High-Density Inference Optimization: Design and implement SageMaker Multi-Model Endpoints (MME) and Inference Components to serve multiple tuned SLMs per GPU, maximizing hardware utilization while minimizing latency.
Deterministic Service Excellence: Treat reliability as a layered engineering problem. Build deterministic "shells" around probabilistic LM outputs, prioritizing data-layer validation and strict serialization.
Automated Evaluation & Observability: Implement "LLM-as-a-judge" patterns and automated benchmarking to detect semantic drift and hallucinations across the fleet before they impact the user.
Standardize the Workflow: Obsess over building reusable patterns and Terraform-based infrastructure that eliminate "snowflake" configurations, allowing us to deploy new specialized AI tasks in minutes.
Agency Strategy: Partner with AI Researchers to find the "Goldilocks zone" for agentic autonomy-balancing the flexibility of LLM tool-use with the precision required for production stability.