דרושים » תוכנה » Machine Learning Team Lead

As a Machine Learning Engineering Manager, you will lead a team focused on the foundational ML & Data layers to power the ranking & recommendation systems in scope. You will drive the development of robust data & ML pipelines at scale, lead the implementation of the tools for ML scientists to test and productionize advanced ML RecSys solutions.

As a technical manager of Machine Learning Engineers and Data engineers, you should be passionate about technology, keep up to date with recent breakthroughs in the field, define and shape the teams ML and platforms roadmap, and not be afraid to get your hands dirty with code when needed.

You are expected to be the focal point for all technical aspects, make sure your team members deliver on their tasks, and work together with other stakeholders to define and shape the roadmap of our products. You will work independently and will also be responsible for making technical decisions within your team.

When it comes to management, your expertise in handling people will motivate and inspire them to reach outstanding success! You should have experience in developing people. You will mentor and coach your team while working closely with a Product Manager.



Key Job Responsibilities and Duties:

Lead and develop a high-performing team, fostering individual growth and collaboration.

Manage and mentor ML engineers and Data engineers, ensuring their professional development and effectiveness.

Develop scalable ML infrastructure and pipelines for efficient data processing and evaluations deployment.

Evaluate architecture solutions based on cost, business needs, and emerging technologies.

Collaborate closely with software engineers to ensure seamless deployment and model inference.

Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.

Collaborate with stakeholders to translate business requirements into viable ML solutions.

Evaluate and integrate new ML technologies to enhance productivity and performance.

We're looking for an experienced and passionate ML Engineering Team Lead to lead our ML Engineering team and shape the next generation of our AI infrastructure. This is a hands-on leadership role where you'll combine technical leadership, software architecture, and people management to build scalable, production-ready AI systems running on edge devices.
About The Role:
Lead, mentor, recruit, and grow a team of software engineers, fostering a culture of ownership, collaboration, and continuous improvement.
Own the team's technical roadmap, architecture, execution, and project prioritization, aligning delivery with business goals.
Design, build, and maintain scalable software and ML infrastructure across cloud and edge environments.
Partner with AI Researchers to productionize Computer Vision and Deep Learning models into reliable, high-performance systems.
Design and optimize inference pipelines with a focus on scalability, latency, and reliability.
Drive engineering excellence through architecture reviews, code reviews, development best practices, and modern AI-assisted engineering workflows.

As a Senior Machine Learning Engineer, you will work closely with top notch engineers and data scientists to design, develop, evaluate and deploy Gen AI-powered solutions for scalable, customer-facing applications. Your work will focus on building and applying state-of-the-art agentic capabilities to drive business impact and improve efficiency.



Key Job Responsibilities and Duties:

Design, develop, and deploy high-quality, performant, and efficient Generative AI-powered solutions and agentic systems into production environments.

Evaluate and define optimal architectural solutions by considering emerging technologies, business needs, and technical requirements for latency, throughput, and scale.

Own services end-to-end, including implementing robust monitoring and maintenance strategies to ensure application and ML health, quality, and performance.

Write and maintain clean, scalable, and well-tested production code, ensuring reproducibility and seamless integration via CI/CD pipelines.

Pioneer and promote best practices and the adoption of cutting-edge technology in GenAI application development.

Collaborate effectively with Product Managers, Data Scientists, and Analysts to understand business requirements and translate them into technical ML and agentic solutions.

Provide technical guidance and mentorship to other engineers, contributing to the team's overall technical development.

As a Machine Learning Scientist II, you will work within a cross-functional team of engineers and product managers to develop, evaluate, and deploy GenAI-powered solutions for scalable, customer-facing applications. Your work will focus on implementing agentic capabilities, contributing to evaluation frameworks, and delivering measurable business impact through data-driven experimentation.


Key Job Responsibilities and Duties:

Contribute to the design and development of end-to-end agentic systems, ensuring code quality and efficiency in production.

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Adhere to and promote established best practices in GenAI application development within the team.

Collaborate actively with team members, participating in code reviews, sharing knowledge, and contributing to a positive team environment.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

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)

We are seeking a Machine Learning Engineer to join the team that builds the predictive intelligence powering the Hello Heart app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations - the systems that determine what users see, when they're nudged, and how their health trajectories are shaped.

This role demands both statistical depth and engineering proficiency - you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities
Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains.
Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results
Write production-grade Python code that is clean, tested, and built for maintainability and scale.
Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor.
Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges.
Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

we are looking for a AI Algorithm Tech Lead.
This is a role in core AI group, which specializes in developing cutting-edge AI solutions in healthcare, enhancing medical imaging and diagnostics. As a Tech Leader in the AI Algorithm Engineering group, you will play a pivotal role in both managing end-to-end AI research and development efforts and hands-on involvement in key-initiatives. Your responsibility will include design, implementation, guidance and mentoring for team members, and representing the AI considerations in cross-team discussions. You will leverage your extensive experience to lead multiple AI projects, and will be a key figure in leading successful execution and in finding creative solutions to difficult bottlenecks, across data, AI, engineering and product considerations.
Responsibilities:
Lead the design, development, and implementation of accurate and robust AI algorithms for medical data with a hands-on approach.
Collaborate with cross-functional teams, including data scientists, software engineers, product managers and clinical data experts, to ensure AI considerations have a profound impact on R&D and Product decisions across the projects.
Design and conduct research and experimentation to improve algorithm performance and accuracy.
Stay at the forefront of the latest advancements in AI and machine learning, particularly in the medical domain, and drive their adoption within the team.
Oversee multiple projects, ensuring successful execution, timely delivery and alignment with product and business objectives.
Provide strategic direction and technical mentoring to elevate the capabilities of the AI team.

We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

Responsibilities:

Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

Were looking for an experienced and passionate Data Group Tech Lead, Staff Engineer to join our Data Platform group in TLV. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of our companys data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives our company toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
We believe three things matter for every role at our company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role youll :
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance
Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across our company to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence across the organization.

‍Were looking for a hands-on Engineering lead to lead our companys growing engineering team. This role combines technical leadership with people management: youll lead a team of engineers both inhouse and offshore, and work closely with the CTO and VP Product leading the company through a transformational phase of significant growth, to deliver a reliable, scalable, and cost-efficient companys Customer Intelligence & Decisioning Platform.
This role is a leadership hands on role: youll get your hands dirty writing code, review and design, and at the same time act as a leader who set standards, processes, coach engineers, and ensure smooth collaboration across the entire team.
Key Responsibilities
Team Leadership & Growth
Manage and mentor the company engineering team.
Create a culture of ownership, collaboration, and accountability. Maintain high standards of work methodologies and delivery.
Establish effective communication and delivery rhythms across our cross functional teams
Hands-On Technical Leadership
Lead & contribute to design, coding, and reviews of the platform, backend , data and MLE functions.
Guide architectural decisions, ensuring scalability, reliability, and cost efficiency, while supporting significant customer growth.
Ensure high-quality engineering standards, CI/CD practices, and observability.
Own Production End-to-End: Delivery & Execution
Own engineering delivery timelines and release cadence.
Coordinate between core and offshore engineers to ensure a unified execution pipeline.
Balance short-term feature delivery with long-term technical health.
Define SLO, build observability, lead on-call, incident response, and postmortems
Collaboration
Work closely with the CTO and VP Product on architecture, integrations, and security.
Work with the CEO to ensure that delivery is aligned with the companys mid-long term vision.
Partner with Product on roadmap prioritization and execution.
Ensure smooth onboarding and ongoing collaboration with customer-facing teams.