לוח משרות דרושים NLP/Machine Learning, לחיפוש זה נמצאו 106 משרות

We are looking for a highly talented Postgraduate Engineer - MSc/PhD to join our Autonomous Systems team and lead the development of a foundation model that will serve as the backbone of our autonomous robot.

Responsibilities:

Process large-scale, multi-modal data from diverse sources.
Design, develop, and build Foundation Models to support perception for autonomous agents.
Build data pipelines, training frameworks, and evaluation methodologies.

We are looking for a Senior Data Scientist to lead the development of machine learning solutions powering real-time decision-making and recommendation systems in industrial production environments.

This is a hands-on role with end-to-end ownership. You will be responsible for the full modeling lifecycle, from problem framing and data exploration to modeling, evaluation, deployment, and production impact. You will work with complex, noisy sensor and control data, building models that must be robust, interpretable, and reliable in real-world conditions.

You will collaborate closely with deep learning researchers, research engineers, and domain experts, and play a key role in shaping how data science is applied across the system.
Key Responsibilities


Design, develop, and evaluate machine learning models for real-time industrial applications
Own the end-to-end modeling pipeline, including problem formulation, data preparation, feature engineering, model development, evaluation, deployment, and post-production monitoring
Translate ambiguous production challenges into well-defined data science problems
Lead feature engineering and data exploration on complex sensor and control datasets
Define KPIs, evaluation frameworks, and experimentation strategies
Ensure model robustness over time, including monitoring, drift detection, and continuous improvement
Work closely with data and research engineers to bring models into production
Drive best practices in modeling, validation, and reproducibility
Communicate insights, trade-offs, and recommendations clearly to stakeholders

Design, implement, and train state-of-the-art neural network architectures for robotic applications, including large multimodal models (vision, language, sensor data) and generative models.
Architect and maintain scalable deep learning frameworks for training on massive, real-world robot datasets.
Develop and implement cutting-edge techniques in areas such as imitation learning, reinforcement learning, self-supervised learning, and video-based models.
Own the full machine learning lifecycle: define data collection and labeling strategies, build high-throughput data pipelines, and establish robust evaluation and deployment protocols.
Collaborate closely with software, hardware, and controls teams to integrate and test your models on physical robots.
Leverage and contribute to internal tooling for large-scale data processing, model experimentation, and continuous integration.

As a Data Engineer , you will own the data lifecycle for Physical AI and humanoid robotics: the pipelines and systems that move data from the moment it is captured to the moment it trains a model.
You will design and scale the pipelines that convert large volumes of simulation and real-robot recordings into state-of-the-art datasets - handling extraction, transformation, feature schemas, statistics, and video at scale. You will build the curation systems that generate and select high-quality training data, and the release tooling that produces versioned datasets.
You will work with various reinforcement learning and computer vision algorithms which generate realistic simulation data for humanoid robotics tasks.
You will build robust, production-grade systems that bridge data producers and consumers, accelerate dataset iteration, and make every dataset that reaches a model trustworthy, reproducible, and traceable to its source.

Required Staff Machine Learning Engineer
Job Description Summary
The Staff Machine Learning Engineer is a senior technical leader responsible for designing, developing, and deploying production-grade AI systems for our medical imaging products. This role focuses on vision-language models, diffusion models, and multimodal learning, working closely with data scientists, software engineers, product leaders, and clinical experts to turn advanced AI methods into robust product capabilities.
Job Description
The Staff Machine Learning Engineer is a senior technical leader responsible for designing, developing, and deploying production-grade AI systems for our medical imaging products. This role focuses on vision-language models, diffusion models, and multimodal learning, working closely with data scientists, software engineers, product leaders, and clinical experts to turn advanced AI methods into robust product capabilities.
Key Responsibilities:
Design, implement, and productionize AI models for clinical applications, including vision and diffusion models.
Own end-to-end model lifecycle: data ingestion, training, evaluation, deployment, monitoring, and iteration.
Work with large-scale medical datasets, ensuring data quality, preprocessing, and efficient pipeline management.
Drive technical decisions and tradeoffs with a focus on scalability, maintainability, and real-world constraints.
Collaborate with clinical AI scientists to translate research innovations into production-ready solutions.
Stay current with advancements in vision-language modeling, GenAI, and related AI technologies.

We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities

AI/ML Architecture: Design and implement scalable AI systems using models such as
LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP)
MCP Integration: Architect and orchestrate model context flows using MCP or similar
frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend
systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and
Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a
culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets,
feedback loops, and model evaluation pipelines

We are looking for an Applied AI Researcher to join our multidisciplinary Antibody team composed of computational biologists, mathematicians and protein designers. In this role, you will drive the development and application of advanced machine learning methods to design and optimize antibodies, contributing directly to cutting-edge therapeutic programs and to the evolution of computational platform.

Develop and apply cutting-edge AI/ML methods for antibody design, including generative modeling and protein language models

Design and implement algorithms for multi-objective optimization of antibodies (e.g., affinity, specificity, stability, developability)

Build tools to support iterative design cycles, combining computational predictions with experimental feedback

Collaborate closely with experimental and computational teams to translate models into impactful design outcomes

Stay current with advances in machine learning, structural biology, and AI-driven protein design

we are seeking an experienced Director of Engineering to lead and scale our software, QA, DevOps, and IT groups in a leading AI startup. This role combines strong people leadership, ownership of execution, and deep technical understanding of production-grade systems.
You will manage engineering managers, drive delivery excellence, and ensure alignment between R&D strategy and business priorities.
Responsibilities:
Lead two engineering groups: IT & DevOps and Software & QA
Manage and develop engineering managers and team leads
Own execution, delivery, quality, and operational excellence across all engineering domains
Drive scalable engineering processes, methodologies, and best practices
Partner closely with Product, Algorithms, Data, Security, and Business stakeholders
Ensure production stability, CI/CD excellence, system reliability, and security
Lead capacity planning, hiring, onboarding, and performance management
Translate high-level strategy into execution plans and measurable outcomes
Foster a culture of ownership, accountability, and continuous improvement
Support architectural and design discussions when needed (hands-on leadership mindset)

Were looking for a Data & Analytics Team Lead to manage and grow our data team and own the data layer behind our AI. Youll lead a team of data analysts responsible for neural-network model analysis, model performance tracking, customer-facing and internal BI. Youll act as both the people manager and the technical lead for the team, setting technical direction while staying hands-on.
Youll thrive in this role if youre passionate about turning data into better AI products, enjoy solving complex analytical problems, and like working at the intersection of Data, Analytics, and Artificial Intelligence.
Responsibilities:
Lead the data strategy supporting AI model development, evaluation, and continuous improvement
Define KPIs and monitoring frameworks for AI model performance.
Analyze NN model outputs, build and maintain model-tracking and performance-evaluation workflows
Drive close calibration with the Algo, Product, and Data Engineering teams to align data definitions, pipelines, and metrics
Stay hands-on with SQL, Python, BI tooling, and the modern data stack
Build and own agentic data workflows - designing the context, tooling, and data access that let AI agents reliably query, analyze, and act on company data

We're looking for a Machine Learning Team Lead to own real parts of it.
What you'll actually do?
Lead, mentor, and grow a multidisciplinary team composed of Data Scientists, Data Analysts, and DLP Analysts.
Define and lead the team's technical vision and execution strategy, driving technical excellence and ensuring alignment with business priorities and product objectives.
Oversee the design, development, validation, and deployment of advanced machine learning solutions for customer-facing products.
Hands-on ownership of current AI services.
Establish and enforce best practices for model development, experimentation, evaluation, documentation, and code quality.
Collaborate closely with Product Managers, Data Engineers, Software Engineers, and domain experts to translate business challenges into scalable ML solutions.
Drive execution and delivery, balancing innovation, technical quality, and time-to-market considerations.
Promote operational excellence by improving the reliability, scalability, and maintainability of production machine learning systems.
Establish scalable engineering processes, workflows, and best practices.
Lead the team transformation from an early-stage organisation into a mature, production-grade AI engineering team.

We're looking for an experienced AI Engineer to join our core team and design, build, and implement the autonomous AI agents that power revolutionary approach to eliminating container vulnerabilities at the source.
Responsibilities:
Explore and prototype cutting-edge AI techniques to tackle our hardest challenges
Architect and develop autonomous AI agents that intelligently eliminate vulnerabilities during container image generation
Collaborate with product and engineering teams to bring AI-driven features from concept to production
Monitor and evaluate AI model performance, implementing feedback loops to continuously improve accuracy and reliability
Develop and maintain machine learning pipelines that process security data and generate actionable insights
Ensure AI systems are robust, reliable, and performant at enterprise scale

We're looking for an experienced Security Researcher to join our core team and deep dive into CVEs, conducting cutting-edge vulnerability research that directly powers Echo's revolutionary approach to eliminating container vulnerabilities at the source.
Responsibilities:
Conduct deep-dive research into CVEs and vulnerabilities, analyzing root causes and attack vectors to inform our AI-powered prevention platform
Perform reverse engineering analysis using tools like IDA Pro, Ghidra, or similar platforms to understand complex security vulnerabilities and exploitation techniques
Research and analyze complex low-level system mechanisms including kernel vulnerabilities, network security issues, and operating system weaknesses
Think outside the box, challenge existing assumptions, and create innovative approaches to vulnerability research and prevention
Work in a unique environment where your research insights are rapidly implemented into our product, creating direct real-world impact from your vulnerability research
Contribute to Echo's technical knowledge base and help establish new methodologies for proactive vulnerability identification and mitigation

What You'll Do
- Design and ship the ML backbone of Gini AI Workers - routing, tool selection, reasoning, memory, evaluation.
- Build evaluation and feedback loops - offline evals, online A/B, regression harnesses, human-in-the-loop labeling pipelines.
- Optimize cost and latency across the agent stack: prompt engineering, model routing (frontier ↔ small ↔ fine-tuned), caching, speculative decoding, distillation.
- Fine-tune and/or RAG-tune models for vertical enterprise tasks (invoice extraction, PO matching, ticket triage, forecasting).
- Own the ML infra - training pipelines, experiment tracking, model registry, deployment, monitoring, drift detection.
- Partner with backend + product to turn research into shipped features on a weekly cadence.

We are looking for a highly skilled ML/Perception Engineer to take ownership of our vision pipeline - from data to deployed models running on embedded systems.
This role is focused on building perception systems that actually work in the field, under real-world constraints.
What You'll Do:
Own the full perception pipeline: data → training → evaluation → deployment
Develop and improve detection and segmentation models using RGB + depth data
Build and maintain a data engine: define labeling strategies, drive dataset quality and consistency, handle noisy and imperfect data
Ensure models generalize to real-world conditions: changing lighting, occlusions, small / hard-to-detect objects
Deploy models to Jetson NX-based systems - ensure compatibility with TensorRT, perform export, validation, and debugging
Collaborate closely with software, robotics, and algorithm engineers to integrate perception into the system