לוח משרות דרושים NLP/Machine Learning עבודה היברידית, לחיפוש זה נמצאו 14 משרות

we are looking for a Machine Learning Engineer / NLP Specialist.
You will own the quality of the conversation itself. That means the end-of-turn model that decides when a caller has finished speaking, the language detection that follows a mid-call switch, the retrieval that grounds an answer, and - most of all - the evaluation harness that tells us whether any change actually helped.
What you'll do:
Improve turn-taking, barge-in handling and interruption recovery
Own language detection and mid-call switching across 150+ languages
Build evals on real call data that predict production behaviour
Run experiments end to end: hypothesis, offline eval, live A/B, decision
Publish what you find - we write about our results

We're looking for an AI researcher to take an integral part in research into how these models behave, how they should be evaluated, and how they can be deployed and used more efficiently across real engineering workloads.
This is a deeply technical research role with direct product impact.
You'll study frontier and open-source models, develop evaluation frameworks, investigate inference and model optimization techniques, explore local deployment strategies, and help answer questions such as:
Which model should handle a particular task?
How much capability do we lose when we quantize it?
Can a smaller local model replace a frontier API for a specific workload?
How should models be routed across latency, cost, privacy, and quality constraints?
How do we measure whether one model is actually better than another for agentic software engineering?
Your work will directly shape model strategy and the intelligence behind our AI infrastructure products.

We are looking for a Generative AI Engineer to lead our AI efforts end-to-end - from infrastructure and production systems to internal adoption and cross-team enablement.
You will play a significant role in building the world's first Consumer Therapy AI Product (someone said RAG? But we mean a real one that works in production...).
This is a critical role in a high-growth, fast-paced B2C startup, requiring a leader who can advocate for Engineering requirements, tech-debt and best practices while promoting a product-mindset and putting our customers front and center.

Required Data Engineering Manager
Data, Ramat Gan, Israel (Hybrid)
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.
If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities:
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

We are looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments.
This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value.
The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface. It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current. You will make pragmatic build-versus-buy decisions and choose the simplest approach-search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation-that meets each use case.
Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve.
What you will do:
Own the architecture and technical roadmap for a secure, reliable, and maintainable local LLM platform deployed in our controlled infrastructure.
Partner with engineering, product, support, IT, information security, legal, and domain experts to prioritize high-value use cases and translate them into measurable product and platform requirements.

We are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.
What you will do:
Define high-value RAN ticket-intelligence use cases, acceptance criteria, evaluation metrics, and quality guardrails.
Build and maintain representative, versioned evaluation datasets using resolved tickets, Root-Cause Analysis, logs, test evidence, code changes, reviews, reassignment history, and outcomes.
Establish current-tool and non-AI baselines before evaluating new LLM, RAG, search, or agent-based approaches.
Evaluate approved internal, commercial, local, and open-source solutions using secure and reproducible data-handling processes.
Measure retrieval quality, groundedness, diagnosis accuracy, citation support, routing recommendations, calibration, abstention, latency, cost, and human effort.
Design held-out, time-based, edge, and adversarial test cases while preventing data leakage and future-outcome contamination.
Analyze failures and turn incorrect conclusions, misrouting, unsupported claims, and missed evidence into prioritized improvements.
Prototype improvements in search, metadata, context construction, prompting, reranking, classification, agent workflows, and model selection.
Develop reusable evaluation pipelines, tools, services, APIs, dashboards, or documented workflows.
Work closely with AI, RAN, QA, System Integration, Release, Field, data, and engineering teams to review results and support evidence-based decisions.

We're looking for an AI Researcher to help design and enhance core intelligence capabilities. You'll work at the frontier of large language models and conversational AI - run experiments, develop novel techniques, and transform research breakthroughs directly into product impact. This is a role for someone who wants to research real-world challenges and ship to production: you'll be pushing the state of the art in domains like conversational dynamics, speech understanding, and agentic question answering, while seeing your work reach millions of language learners.
What You'll Do:
Drive original AI research across LLMs, dialogue systems, speech, and language understanding - from hypothesis through design and all the way to deployment
Design and run rigorous experiments; develop novel model architectures, fine-tuning strategies, and training methodologies
Own the full research lifecycle - ideation, experimentation, evaluation, and deployment into production
Apply and extend state-of-the-art techniques (RL, instruction tuning, RAG, agentic workflows, multimodal models) to real user-facing challenges
Collaborate with engineering and product to transform research findings into shipped features
Define research direction in your area and contribute to long-term AI roadmap

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.
This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.

We are looking for an engineer who can take ownership of a solution from understanding the business problem through production delivery and continuous improvement.
AI-assisted development tools are a core part of how you work. You use them daily, critically, and effectively to improve the quality and speed of software delivery.
You will report to a Domain Architect and work closely with senior engineering, product, and business leaders on AI initiatives and other high-impact challenges.
You will contribute across the full lifecycle: problem definition, solution design, implementation, evaluation, deployment, monitoring, and production support.
What youll do:
Own the full loop - understand the problem, define the solution, architect it, ship it, monitor it. No one hands you a spec
Build with AI as your primary tool - Claude Code, Cursor, whatever gives the most leverage; do in a day what used to take a team a week
Co-design with stakeholders and the business - decision logic, risk thresholds, success metrics
Build the evaluation harness before you ship - if you cant measure it, its not done
Own production from day one - monitoring, alerting, observability; youre on call for what you build
Raise the bar - in design reviews, in code, and in how the team works

Were hiring an AI Backend Engineering Manager to guide and grow a high-impact ML team driving AI-powered innovation across B2B SaaS platform. Youll lead the design and delivery of AI solutions while mentoring engineers and setting the technical direction for AI-first development at scale.
This is a leadership role with a balance of hands-on engineering and team management, perfect for someone who thrives on solving technical challenges, inspiring a team, and shaping the future of AI in fintech automation.
What You Will Do:
Lead & Mentor: Manage, mentor, and grow a team of AI/ML/Backend engineers, fostering technical excellence and career development.
Set Technical Direction: Define the ML strategy, ensuring best practices in architecture, frameworks, and operationalization.
Build and deploy AI-based solutions: Oversee the development and deployment of GenAI/LLM-powered solutions that address real-world challenges across products.
Scale & Operationalize: Establish scalable ML infrastructure, CI/CD, observability, and data pipelines for high-availability production systems.
Collaborate Cross-Functionally: Partner with product managers, engineers, and business stakeholders, clearly communicate progress, challenges, and outcomes.