דרושים » הנדסה » ML Engineering Team Lead - Applied AI Engineering Group

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 mission - this role is for you.
: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.

It starts with you - an AI Engineering Team Lead responsible for leading a team building production-grade agentic systems. This role combines hands-on technical leadership with people management, ownership of architecture, delivery, and quality across agents, MCP tools, agentic workflows, RAG systems, and evaluation.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Lead and mentor a team of AI Engineers building agentic systems and MCP tools.
Own technical direction for agent architectures, workflows, and LLM evals.
Review designs and code; ensure production readiness, reliability, and security.
Drive adoption of frameworks such as LangChain, LangGraph, or equivalent.
Set standards for RAG pipelines, vector databases, and retrieval quality.
Partner with product and platform teams on roadmap, scope, and delivery.
Balance hands-on contribution with people management and execution.
Build a culture of strong engineering practices, measurement, and iteration.

We are looking for a hands-on Tech Lead to join the Core Platform team within our 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 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.

Were hiring a Machine Learning 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 ML 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.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI systems and ML pipelines to join our AI group. You'll be responsible for designing, building, deploying, and maintaining production-grade AI systems and ML pipelines. Youll work closely with data scientists to translate research into scalable solutions and manage model deployment in both cloud and on-prem GPU environments.
:Responsibilities
Design, build, and deploy production-grade ML models, AI agents, and end-to-end pipelines across cloud and on-prem GPU environments.
Maintain and optimize ML systems for performance, scalability and reliability, including model validation, inference speed, and resource efficiency.
Develop monitoring and observability tools such as alerts and performance metrics to ensure system stability in production.
Create and integrate APIs for ML services within microservice-based architectures.
Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems.

Were hiring a ML Engineer to accelerate AI-driven innovation across Stamplis B2B SaaS platform.
Youll be at the forefront of building intelligent systems that power core product experiences and automate internal operations, driving efficiency, speed, and scale across the organization. This is a high-impact, hands-on role in a fast-growing, AI-first company where machine learning is a foundational pillar, not a bolt-on feature. You'll partner with product, engineering, and operations teams to design and implement powerful ML and LLM-based solutions that make a measurable difference.
What You Will Do:
Build Intelligent Systems: Design and develop ML/LLM-powered solutions that solve real-world challenges across Stamplis product and internal workflows.
Own Full Lifecycles: Take projects from concept all the way to production, including model training, evaluation, integration, and monitoring.
Leverage State-of-the-Art Tools: Work with leading frameworks like LangChain, Hugging Face, TensorFlow, and PyTorch to deliver cutting-edge functionality.
Collaborate Cross-Functionally: Partner with product managers, engineers, and stakeholders to embed AI capabilities into user-facing features and backend services.
Ship at Scale: Build and maintain scalable APIs and services, integrating best practices in CI/CD, observability, and cloud infrastructure.
Report with Impact: Share progress, challenges, and results clearly with technical and executive stakeholders.

As a Senior MLOps Engineer , you will design, build and scale our machine learning infrastructure. You will lead the end to end model lifecycle, from data and training to deployment and monitoring, ensuring reliable and high performance ML systems in production. You will work closely with our research and engineering teams to bring advanced AI models into production and help shape the future of our AI capabilities.

we are seeking an experienced and visionary Head of Research to lead, shape, and advance the future of AI innovation across the organization.
In this role, you will own the research strategy end-to-end - from long-term roadmap design to hands-on exploration, and guide a multidisciplinary team of NLP research engineers and linguists toward breakthroughs that elevate our core medical coding engine.
You will play a key role in the company, balancing exploratory research with high-impact product delivery. You will partner with Product, Engineering, and executive stakeholders to identify strategic opportunities, drive prioritization, and translate research capabilities into real-world outcomes at scale.
Responsibilities:
Manage and mentor a multidisciplinary research team while driving both technical excellence and personal growth.
Define the long-term research roadmap aligned with product and business objectives.
Lead the full research lifecycle, from ideation and feasibility studies to experimentation, evaluation methods, and production-ready delivery.
Evaluate and introduce new research capabilities and emerging trends such as LLM-based reasoning, semantic/graph modeling, hybrid rule-learning, and other frontier techniques.
Continuously track scientific trends to maintain a state-of-the-art research practice.
Collaborate closely with Product and Engineering teams to translate domain challenges into algorithmic solutions with measurable product impact.
Represent the Research function at leadership forums, communicate priorities and outcomes, and align stakeholders around research strategies and milestones.

We're looking for talented, versatile and highly independent Machine Learning Engineers to join our winning team. You will be in charge of end-to-end development and you'll have a massive impact on the product and the technical decisions made.
The ML Engineers are part of our infrastructure group, working closely with the exceptional research team, taking trained models and scaling them out to production.
Responsibilities
Take ownership of the entire machine learning engineering lifecycle - from building scalable training and evaluation pipelines to deploying models in production, with robust monitoring and maintenance systems.
Help in creating scalable solutions by enabling us to continuously increase the accuracy of our algorithms across thousands of clinics.
Designing a secured large-scale system that is suitable for sensitive patient data.
Continue to enhancing our deep learning infrastructure to supports our AI models at scale, including CI/CD, automation, testing and monitoring.
Collaborate with the research, medical and product teams in implementing ML solutions to the digital health space.

We are seeking a highly skilled Senior Networking AI Platform Engineer to join our Applied Networking AI group. In this role, you will help design and develop cutting-edge AI solutions, integrating them seamlessly into a variety of products. Youll collaborate closely with multi-functional teams of data scientists, software engineers, and DevOps professionals to ensure the efficient deployment, monitoring, and optimization of machine learning (ML) models.
As a key contributor, you will drive the entire software development lifecycle-from conceptualization and architecture to implementation and production-while working closely with engineering teams to solve complex problems and help build a successful company practice.
What you'll be doing:
Lead the design, development, and deployment of robust software systems across different platforms and environments
Architect, design, and implement scalable and high-performance software solutions, handling complex requirements and integrating various subsystems
Ensure systems are maintainable, flexible, and well-documented, with an emphasis on performance and security
Adapt to new tools, technologies, and frameworks, and be capable of taking ownership of the development process from conception to deployment
Supply innovative ideas and solutions, driving continuous improvement in both code quality and system efficiency
Develop and maintain scalable infrastructure for handling and deploying security and networking ML models in production, ensuring high availability, scalability, performance.
Design and implement data pipelines to efficiently process and transform large volumes of data for training and inference purposes.
Optimize and fine-tune ML models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.
Collaborate with data scientists and software engineers to operationalize and deploy ML models, including model versioning, packaging, and integration with existing systems.