לוח משרות דרושים NLP/Machine Learning משרה מלאה

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.

Were looking for a Senior AI/ML Architect to help shape how our customers implement AI. In this role, youll design scalable, end-to-end ML solutions, from classical models to generative AI, and guide customers from concept to production. Youll work closely with C-level leaders, data scientists, and engineers to deliver impactful, real-world AI outcomes.
📍 Work location - hybrid from Tel Aviv
If you are interested in this opportunity, please submit your CV in English.
Responsibilities
Design and deliver end-to-end ML solutions, including classical ML, deep learning, and generative AI
Create technical proposals, reference architectures, and deployment plans
Lead workshops, POCs, and architecture reviews with startup customers
Translate business needs into scalable ML systems
Implement distributed training workflows and LLM serving infrastructure
Build MLOps pipelines for tracking, versioning, and monitoring
Provide technical guidance to customers and internal teams
Stay up to date on ML trends and contribute to internal enablement
Frequent travels - locally (on-demand to meet with customers and partners and attend local events) and abroad (at least once a quarter).

If youre looking to build something big, solve real problems, and grow fast-wed love to meet you.

You will own AI infrastructure and agentic interaction layer on top of our Data Platform.

The data layer is the product.
The AI layer is the multiplier.
The interaction model between them is the future.

Your responsibility is to design and build:

The AI infrastructure that enables safe, scalable, production-grade AI features

The ability to interact with Data Layer through LLMs and agentic workflows

Guardrails, governance, and system boundaries for AI-driven data access

The foundations that allow AI to be powerful without compromising correctness or trust

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing Immunais AI models. You will own and evolve Immunais 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.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Own & Evolve Benchmarking - Design, build, and maintain Immunais 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.

As the Product Manager for the AI models of core product offering, you will be responsible for defining and driving its product strategy, requirements, roadmap and delivery.

The product focuses on addressing real-world challenges in drug development by leveraging AI foundation models and multi-omic data to map and predict human immune response.

You will be working closely with our team of Data Scientists, Computational Biologists, Deep Learning Researchers and Engineers.

Location: Ramat Gan, Israel (Hybrid model)

What will you do?

Set the long-term vision and strategy for AI models, ensuring alignment with company goals.
Define product requirements and build a clear, prioritized roadmap for model development and delivery.
Lead benchmarking efforts to evaluate model performance against internal baselines and external best-in-class models.
Collaborate with AI research, data science, engineering, and biologists to translate scientific advances into scalable product capabilities.
Oversee the end-to-end lifecycle of AI models, including data strategy, training priorities, evaluation criteria, releases, and post-deployment monitoring.
Identify, validate, and prioritize high-impact use cases where improved model capabilities create meaningful business or scientific value.
Communicate strategy, progress, and key decisions to leadership and cross-functional stakeholders to drive alignment.

We are looking for a driven and experienced leader to manage a growing interdisciplinary team of LLM Agentic AI Researchers, Computational Biologists, and Immunologists. This teams mission is to build a cutting-edge agentic reasoning system powered by Large Language Models (LLMs), capable of generating structured, biologically grounded sequential explanations based on predictions from proprietary foundation model-trained on hundreds of millions of immune cells. The product the team builds is a pillar of AI strategy, with the potential to redefine the way a drugs mode of action is understood.

Location: Ramat Gan, Israel (hybrid model)

What You'll Do:

Set the Vision - Collaborate with product leadership to transform bold ideas into AI capabilities that impact real-world medical decisions.
Build and Lead the Team - Coach, build and inspire a high-caliber team across LLMs, data science, and Immunology. Cultivate a culture of excellence, curiosity, and execution.
Drive Innovation - Lead from the front-design architectures, write prototypes, and guide technical direction. Lead the team in building agents that reason about biological systems at scale.
Ship Transformative Products - Deliver AI products & solutions that move the needle and its partners - from static biological associations to dynamic, explanatory models of causality.
Showcase Leadership - Represent the team in top-tier scientific conferences and publications, communicating groundbreaking research to the world.

Were looking for an Applied Data Scientist to help us build and scale the core marketing science products that power our platform. This role sits at the intersection of data science and engineering: owning statistical models end to end, deploying them into production, and turning advanced analytics into reliable, customer‑facing capabilities. Your work will directly influence how thousands of brands measure ROI and allocate millions in marketing spend.

What Youll Do:
Build marketing measurement and optimization products (e.g., attribution models, MMM, incrementality testing, forecasting systems)
Develop and deploy statistical and machine learning models to production
Create scalable data pipelines and APIs that serve real-time analytics
Partner closely with Product, Engineering, and other stakeholders to translate business questions into clear analytical solutions.
Own features end to end from statistical design and validation through deployment, monitoring, and iteration.

As a ML Engineering Team Lead at our company, you will lead a multidisciplinary team of algorithm and software engineers responsible for designing, building, and operating the next-generation content creation engine. The team focuses on leveraging agentic systems and advanced ML technologies to orchestrate complex, end-to-end production workflows at scale.
In this role, you will translate high-level product and business goals into robust, production-ready solutions, working hands-on across the full lifecycle - from research and system design to deployment, monitoring, and continuous improvement. You will collaborate closely with Product, Platform, Data, and other R&D teams in a fast-paced, agile environment, ensuring that algorithms and software are seamlessly integrated into real-world, high-throughput systems.
You will combine technical leadership, architectural thinking, and people leadership to drive innovation, execution, and delivery, while shaping how intelligent, automated content is created and scaled across our companys products.
What youll do:
Lead and mentor a multidisciplinary team of algorithm and software engineers, fostering ownership, technical excellence, and professional growth.
Own the delivery of complex, end-to-end systems for automated content creation, from early design and experimentation through production deployment, monitoring, and iteration.
Drive the design and orchestration of agentic systems, working hands-on with the team to integrate ML models, business logic, and software infrastructure across research, engineering, and production at scale.
Translate product and business challenges into technical solutions, defining system architecture, prioritizing initiatives, and guiding execution in an agile environment.
Ensure production readiness and reliability, overseeing deployment, monitoring, performance, and continuous improvement of algorithms and services in a live environment.
Collaborate closely with Product, Platform, Data, and other R&D teams, aligning technical execution with product strategy and business goals in a multidisciplinary setting.
Continuously evolve technically and professionally, encouraging knowledge sharing, joint problem-solving, and awareness of emerging trends in ML, agentic systems, and large-scale software architectures.

we are looking for a Senior NLP Algorithm Developer to join us in research and development of our next-generation automatic sports video content engine. If you are passionate about state-of-the-art NLP and Generative AI research - we are looking for you!
What youll do:
The teams main focus: NLP & Generative AI.
Research and develop end-to-end real-time solutions for sports content generation automation using state of the art deep learning algorithms for text, speech, vision, audio and data analysis.
Full R&D cycle, from problem definition and research to implementation and productization with a high level of autonomy.
In addition, part of the role & responsibilities will be:
Design, develop and implement state-of-the-art Generative models, prompt engineering and classical ML/NLP techniques to create high-quality solutions for demanding product-oriented NLP tasks.
Engage in collaboration with product, account, and customer success teams to establish success criteria and convert them into measurable objectives and quantitative measures.
Conduct training and evaluation of models within practical production scenarios.

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.