לוח משרות דרושים NLP/Machine Learning משרה מלאה, לחיפוש זה נמצאו 108 משרות

Required Senior Agentic Systems Engineer
Israel - Raanana
We dont limit our challenges. We challenge our limits. Always. Were ambitious. Were game changers. And we play to win. We set the highest standards and execute beyond them. And if youre like us, we can offer you the ultimate career opportunity that will light a fire within you.
So what is the role all about?
The Senior Agentic Systems Engineer is a hands-on engineer who designs, builds, and operates AI-powered tools and workflows for engineering teams.
Were looking for builders who are not only users of AI tools, but can design autonomous agentic systems, optimize model usage, create reusable agents and workflows, and orchestrate complex multi-step work across multiple tools and agents.
Youll be a one-person-army builder: able to understand a problem, define the approach, build the backend, create a simple UI when needed, integrate APIs, connect agents, automate workflows, and deliver working tools quickly.
How will you make an impact?
Build centralized AI-powered tools for engineering teams.
Create and maintain agents, skills, hooks, MCP servers, plugins, and workflow automation.
Design autonomous agentic systems for internal engineering use cases.
Build multi-step AI workflows across code, documentation, Jira, GitHub, CI/CD, and internal systems.
Build tools for PR review support, test generation, documentation updates, spec creation, codebase understanding, and developer productivity.
Learn from local teams and convert successful solutions into reusable centralized assets.
Integrate AI tools with GitHub, Jira, Confluence, CI/CD, internal portals, and engineering systems.
Optimize AI workflows for cost, latency, quality, and reliability.
Apply token economy thinking when designing agents, prompts, MCP usage, RAG flows, and tool integrations.
Contribute to the internal AI marketplace and reusable asset catalog.
Work directly with engineering teams, collect feedback, and iterate quickly.

A significant part of the role involves building end-to-end integration flows and tests, particularly around token generation pipelines and system orchestration, as well as contributing to intelligent system behavior such as hardware selection and execution strategies. The role also includes developing system-level logic in Python for multi-tenant management, caching strategies, and service lifecycle management across the platform.

***This is not a Data Science position***

Responsibilities:

Build and maintain end-to-end integration flows across the AI inference pipeline (serving, orchestration, APIs, and infrastructure)
Design, implement, and optimize LLM inference workflows, including prefill and decode stages
Improve system performance with focus on throughput, latency, and interactivity
Write production-grade components in Python and integrate them into the broader system
Contribute to system-level logic such as smart hardware selection and execution strategies
Integrate models (open source and custom), services, and APIs into cohesive, reliable end-to-end application pipelines

We are looking for a Senior Software Engineer to help build and optimize large-scale, high-performance GenAI infrastructure and inference systems on Kubernetes.

As AI workloads increasingly move toward Kubernetes-native infrastructure, we are building systems that support distributed inference, performance optimization, reliability, observability, and production-grade deployment at scale.

This role is ideal for an engineer who can reason deeply about systems, performance, tradeoffs, and reliability, and who is comfortable owning difficult technical decisions end-to-end.

You will work across inference serving, distributed systems, optimization, and Kubernetes-native AI infrastructure.

What Youll Do

Build and optimize high-performance Kubernetes-native GenAI inference systems
Work with modern inference stacks such as vLLM, SGLang, TensorRT-LLM, and related tooling
Work with Kubernetes-native distributed LLM inference frameworks such as llm-d and NVIDIA Dynamo
Design and implement optimization algorithms and performance improvements
Improve reliability, observability, deployment, and operational maturity of AI systems
Make architectural decisions and take ownership of technical outcomes
Collaborate with a small, senior engineering team focused on performance and production quality

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

we are looking for a Senior ML Engineer.
Responsibilities:
As a Senior Machine Learning engineer at Substrata, you will be working directly with our Head of R&D and will take a major role building our Pragmatic Intelligence Engine using and experimenting with S.O.T.A Deep Learning architectures, including: Multi-Layer Perceptron Neural Network, LSTM, mLSTM, CNN, RNN, VDCNN, etc.

What you will do
You will lead a team of experienced Data Scientists while remaining deeply involved in the technical work.

This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.

You will work on core systems that operate at a massive scale, where:

Data is abundant, but labels are scarce and expensive

problems are long-tail and ambiguous

Systems must meet strict latency and cost constraints (pre-bid)

Your responsibilities include:

Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps

Design and build models across computer vision, NLP, and multimodal pipelines

Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment

Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)

Improve model quality (precision/recall) while balancing cost, latency, and scale

Drive automation systems (auto-labeling, auto-curation, retraining loops)

Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems

Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward

Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems

we are seeking an experienced and visionary ML Engineer to join our dynamic technology organization. The successful candidate will be a part of a team of talented AI Engineers, Building agentic AI solutions, driving innovation and delivering business value through advanced Generative AI solutions & machine learning techniques. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge Gen AI and LLM methodologies and a passion for continuous learning and development.

Responsibilities

Build the solution: Own end-to-end technical delivery of agentic systems, from source-system integration through agent design, development, evaluation, and production deployment.
Integrate AI systems with source systems (Salesforce, Databricks, Splunk, internal APIs, business applications). Handle agent harness and orchestration.
Handle the operational handover to the business function and any necessary support transition.
Collaborate across teams: Partner closely with data engineering, platform, security, and business teams to align on requirements, dependencies, and integration points.
Engage stakeholders: Gather requirements directly from business functions, communicate technical trade-offs clearly, and keep stakeholders informed on progress, risks, and timelines.
Uphold quality and reliability: Establish and maintain best practices for code quality, testing, evaluation, monitoring, and observability of deployed AI systems.
Contribute to the team's technical growth through knowledge-sharing and help shape engineering standards and reusable patterns.
Stay current: Continuously evaluate emerging Gen AI, LLM, and agentic frameworks, and recommend tools and approaches that improve delivery speed and solution quality.

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. 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 our 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 our 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 our risk management system. - Collaborate with Principal ML engineers from across the company to adopt best industry and our 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
Identify and Initiate New Business Opportunities: - Collaborate with our 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.

Our Fundamental AI Research (FAIR) organization is seeking a Research Engineer to drive advancements in generative models. The role involves working across the full spectrum of research, engineering, and optimization for frontier model efforts.
Research Engineer, Fundamental AI Research (FAIR) - Generative Models/LLM Acceleration Responsibilities
Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance
Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization
Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
Take a significant role in components, features, or systems with good end-to-end understanding
Contribute to publications, open-sourcing initiatives, and mentor other team members.

We are looking for a Software Engineer to join our core Algorithm team as an dedicated data-core / platform engineer. You will support the algorithm engineers developing detection algorithms to design and build the infrastructure they run on: the object-detection (OD) pipeline, its orchestration and deployment, its data and model catalog, and the benchmark and labeling systems that drive model improvement. This is a high-autonomy role with real, end-to-end ownership of production systems from day one.

The technologies listed in this description are examples of our day-to-day - not a rigid checklist. We care far more about how you think, how you plan, how you debug, and how fast you learn than about which specific tools you have already used. If you love the craft of making things work and want to go deep, you can learn the rest here.

In the AI era, being a strong engineer means more than writing great code. It means operating as an architect who directs AI agents - designing the solution with clarity, then guiding them to execute it at a level and speed that wasn't possible before. We are building a culture where this is the norm, and we're looking for someone who is excited to work and grow in that direction.



What You'll Do

Take on hard, open-ended infrastructure challenges and make them work - designing, building, decoupling, and hardening the systems behind our object-detection pipeline, from data and model management to benchmarking, so everything runs reliably at scale.
Architect and build the backbone of the OD pipeline - orchestration (Airflow on Kubernetes), data plumbing (S3 / PostGIS / SQS), CI/CD, and deployment across multiple environments - designing clean interfaces and data contracts the algorithm team can build on with confidence.
Debug across the whole stack, wherever the problem leads - a stuck DAG, a flaky pipeline stage, a slow query, a GPU/driver mismatch - and turn one-off firefights into lasting fixes and better observability.
Own the data and model lifecycle: versioned datasets and model weights with clear provenance, and the labeling → export → retraining loop that keeps the models improving.
Learn fast and go deep. Pick up new tools and new layers of the stack as the work requires, and raise the team's engineering and operational standards as you go.
Partner closely with algorithm engineers and the data-collection / labeling operations team to turn research prototypes into robust, scalable production systems.
Integrate AI tools into your workflow and grow into operating as an architect who directs AI agents - designing the solution, then guiding them to build it.

Were looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a Staff AI Engineer, Prompt, you will be tasked with driving the mission of Prompt Security by SentinelOne, where we empower organizations to safely harness the transformative power of Generative AI. As part of our one-stop security platform, you will help provide visibility, governance, and real-time protection against all GenAI concerns, enabling organizations to innovate and prosper in the age of AI without compromising on security. As the Generative AI security field continues to expand, SentinelOne is at the forefront of this revolution. This market's rapid growth demands agility and flexibility from our team. In your role as a Staff AI Engineer, you'll navigate an exciting landscape where innovation is constant, working on the frontline to develop and refine our security engine. We're looking for a builder at heart, someone who learns by shipping, gets hands-on with the latest AI tooling, and thrives in fast-paced environments. If you're passionate about staying ahead in a dynamic market and have the skills and agility to keep pace with its evolution, this is your arena to shine.

You will be part of the DevEx team which is in charge of significantly improving the productivity, efficiency, and satisfaction of developers across the organization. You will take a leading role in the team, build easy-to-use platforms, lead wide-impact projects, mentor team members, developers and keep innovating with new tools, workflows, deep dives and adHoc POCs. This role requires a blend of deep hands-on technical expertise, strategic vision and mentoring skills to drive the team to excellence in a culture of developer-centric workplace.



Key Responsibilities:

Lead Initiatives: Own the design and delivery of high-impact developer platforms, CI/CD evolution, AI tooling and more, from ideation through production rollout and adoption.
Drive Excellence: Set a high bar for system design, code quality, scalability, and reliability across platforms. Act as the go-to expert for complex technical challenges.
Hands-on: Design and build scalable, self-service developer platforms, with strong focus on usability, performance, observability and cost efficiency.
Mentorship and Guidance: Mentor engineers through design reviews, pair programming and knowledge sharing. Elevate the technical capabilities of the team.
Cross-Team Focal Point: Collaborate closely with stakeholders and infra teams to influence architecture, improve client experience and ensure solutions are widely adopted.
Lead by Example: Demonstrate strong ownership, execution speed and high-quality delivery standards in day-to-day work.

Our team is looking for a skilled professional to join the AI software group. In this role, you will focus on our AI kernel compilers, ensuring our hardware delivers peak optimization for sophisticated and custom AI kernels. You will work extensively with various kernel infrastructures, including Triton and CUDA, while collaborating closely with our AI graph compiler teams.



Responsibilities
Develop our AI kernel compilers and related components in the SW stack.
Drive the performance optimization of AI models running on our unique architecture.

We're looking for a software engineer who specializes in machine learning - someone whose core strength is writing excellent, production-grade code. You'll own the engineering quality of our ML systems: building, maintaining, and monitoring the services that run our models in production, and helping our data scientists ship code that's readable, maintainable, and built to last.

Responsibilities

Transition ML workloads from research to production - ensuring scalability, efficiency, and reliability.
Design, build, and maintain ML services, their infrastructure, and monitoring across their lifecycle.
Integrate ML into our production systems, working closely with the engineering and devops teams.
Own the data infrastructure and tooling our ML systems rely on, and shape unstructured data into a form ready for analysis.
Help data scientists write readable, maintainable code, and raise software engineering standards across the team.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.