דרושים » תוכנה » Senior ML Engineer

As a Big Data & GenAI Engineering Lead within our company's Data & AI Department, you will play a pivotal role in building the data and AI backbone that empowers product innovation and intelligent business decisions. You will lead the design and implementation of our companys next-generation lakehouse architecture, real-time data infrastructure, and GenAI-enriched solutions, helping drive automation, insights, and personalization at scale. In this role, you will architect and optimize our modern data platform while also integrating and operationalizing Generative AI models to support go-to-market use cases. This includes embedding LLMs and vector search into core data workflows, establishing secure and scalable RAG pipelines, and partnering cross-functionally to deliver impactful AI applications.
As a Big Data & GenAI Engineering Lead in our company you will...
Design, lead, and evolve our companys petabyte-scale Lakehouse and modern data platform to meet performance, scalability, privacy, and extensibility goals.
Architect and implement GenAI-powered data solutions, including retrieval-augmented generation (RAG), semantic search, and LLM orchestration frameworks tailored to business and developer use cases.
Partner with product, engineering, and business stakeholders to identify and develop AI-first use cases, such as intelligent assistants, code insights, anomaly detection, and generative reporting.
Integrate open-source and commercial LLMs securely into data products using frameworks such as LangChain, or similar, to augment AI capabilities into data products.
Collaborate closely with engineering teams to drive instrumentation, telemetry capture, and high-quality data pipelines that feed both analytics and GenAI applications.
Provide technical leadership and mentorship to a cross-functional team of data and ML engineers, ensuring adherence to best practices in data and AI engineering.
Lead tool evaluation, architectural PoCs, and decisions on foundational AI/ML tooling (e.g., vector databases, feature stores, orchestration platforms).
Foster platform adoption through enablement resources, shared assets, and developer-facing APIs and SDKs for accessing GenAI capabilities.

We are looking for an experienced AI/ML Engineer to join our Applications team within our platform engineering group. In this role, you'll be responsible for designing, building, deploying, and maintaining production-grade AI systems and ML pipelines. You'll translate cutting-edge data science research into practical, scalable solutions, handling model deployment both in cloud environments and on-premises using GPUs and CUDA. Youll optimize and implement ML models, workflows, and AI agents to ensure high performance and reliability in production environments.
Responsibilities:
Design, implement, and deploy ML models, AI-driven applications, AI workflows, and LLM-based agents into production.
Build, manage, and maintain robust ML pipelines and systems.
Deploy models on cloud and on-premises GPU servers.
Optimize system performance, including model inference, scalability, and resource utilization both on cloud and on-premises.
Develop and maintain services, APIs and integrate ML models into microservices-based applications.
Collaborate with cross-functional teams including data science, backend, DevOps, and platform teams.
Stay up to date with the latest developments in AI, machine learning, and related fields, focusing on LLMs, exploring how emerging technologies can be applied to improve products and services.

We are seeking a Senior Data Scientist to lead the development of personalization models that power our AI agents. In this role, youll design and deploy intelligent systems that understand each travelers preferences, intent, and behaviortransforming complex user journeys into effortless, hyper-personalized experiences.This is a high-impact, cross-functional role working closely with product managers, engineers, designers, and the AI/LLM platform team to create deeply personalized, autonomous travel assistance that feels like magic (but is all math and modeling underneath).
What Youll Do:
Design and build machine learning models that power personalization across our agentic platform, including traveler profiles, flights & hotels recommendation engines, and behavioral prediction systems.
Develop algorithms that adapt and learn from user behavior over time, channels, and modalities (text, voice, action).
Collaborate with PMs, engineers, and conversational designers to integrate ML into real-time decision loops, conversational agents, and proactive UX flows.
Own experimentation pipelines and performance evaluation, AB tests, offline metrics, and production agent behavior.
Work hands-on with structured and unstructured data (e.g., travel history, chat logs, user feedback, booking behavior) to extract insights and drive model improvement.
Define and maintain robust MLops practices: monitoring, retraining, feature pipelines, and real-time inference.
Stay current with the latest in recommender systems, LLM integration, embeddings, retrieval augmentation, and agentic reasoning to continuously evolve our approach.

Were hiring a Machine Learning Engineer to accelerate AI-driven innovation across our B2B SaaS product and the broader organization.
This is a hands-on, end-to-end development role where you'll lead the implementation of ML/LLM-based solutions that boost operational efficiency and deliver exceptional product experiences. As an AI-First company, we embed AI across every department and workflowyou'll be at the heart of this transformation.
You will collaborate closely with product managers, engineers, and stakeholders to identify impactful AI opportunities, build state-of-the-art systems, and scale them across the organization.
What You Will Do:
AI-First Development: Design and implement POCs and production-ready ML/LLM solutions that address real-world business problems.
End-to-End Execution: Own projects from research through deployment, including model training, evaluation, integration, and monitoring.
Tool Evaluation: Stay current with state-of-the-art ML/LLM tools and frameworks (e.g., LangChain, Hugging Face Transformers, TensorFlow, PyTorch) and recommend their application.
Collaboration: Partner with engineering, product, and operations teams to embed AI into user-facing features and internal workflows.
System Integration: Build scalable microservices and APIs to deliver AI capabilities across the product.
Operationalization: Contribute to DevOps practices for CI/CD, observability, and ongoing optimization of AI models.
Strategic Reporting: Communicate progress and insights directly to senior management.

Were seeking an experienced AI/ML Platform Engineer to join our Foundations team, the group behind our next-generation GenAI platform powering innovation across the company and beyond. This team is building scalable, high-performance AI systems for both internal users and external customersdesigned to run seamlessly across cloud and on-premise environments using the latest advancements in hardware. In this role, youll lead efforts in distributed training, inference at large scale, resource optimization, and robust model lifecycle management using MLOps best practices. Your work will be critical to accelerating research, supporting production-grade AI infrastructure, and driving the development of our internal AI ecosystem.
Responsibilities:
Architect and build scalable ML infrastructure for training and inference workloads across heterogeneous compute environments (on-premise and cloud).
Design and implement distributed systems to support model lifecycle management from data ingestion and preprocessing, to training orchestration and deployment.
Optimize performance and cost-efficiency of large-scale model training and serving pipelines using technologies like Ray, Kubernetes, Spark, and GPU schedulers.
Collaborate with AI researchers, data scientists, and product teams to understand their workflows and translate them into reusable platform services and APIs.
Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems.
Contribute to the long-term vision and technical roadmap of the ML platform, ensuring it evolves to meet the growing demands of AI across the company.

We are looking for an experienced MLE Team Lead to lead our Machine Learning Engineering team as part of the Infrastructure group. AI/ML are core components of Navina unique technology, and a significant part of our technical edge. This is a unique opportunity to take a leadership role in building the infrastructure that powers the models driving Navinas products.

Youll lead a team of ML engineers in designing scalable production systems, collaborating with cross-functional stakeholders, and mentoring team members as we scale our ML platform and capabilities.

Responsibilities:
Provide technical leadership and team management for the Machine Learning Engineering team, including mentoring team members, and encourage ongoing personal and professional growth.
Define and drive the roadmap for ML infrastructure in collaboration with other technical leaders and data science team, ensuring alignment with broader engineering and organizational goals.
Remain actively involved in the architecture and development of systems that enable scalable and reliable model training, deployment, serving, and monitoring.
Own the engineering aspects of the ML lifecycle, including the design of efficient production-grade deployment mechanisms, robust observability and maintenance practices.
Work closely with data science, product, medical, and dev teams to understand evolving needs and deliver infrastructure and tooling that improve development velocity and model reliability.
Continuously evaluate emerging tools and best practices in ML infrastructure and thoughtfully integrate relevant technologies to strengthen the teams capabilities.

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.

As a Software Engineer in the team, you will contribute to the design, development, and operation of our comprehensive data fabric an enterprise-grade system that enables data-driven decision-making and high-impact product features across our company. You'll work closely with a cross-functional team of engineers, product managers, and data experts to build scalable data infrastructure and intelligent data management capabilities that power internal and external services. You will:
Drive the development of sophisticated and robust logic and backend infrastructure that supports large-scale data and compute workflows.
Collaborate across teams to design solutions that meet performance, reliability, and security requirements.
Contribute to the automation and optimization of engineering processes, improving deployment velocity and system health.
Participate in the full software development lifecycle, from ideation and design to deployment and monitoring.
Help shape engineering standards, technical architecture, and operational best practices within the team.

Required Data Infra Tech Lead
A day in the life and how youll make an impact:
Were seeking an experienced and skilled Data Infra Tech Lead to join our Data Infrastructure team and drive the companys data capabilities at scale.
As the company is fast growing, the mission of the data infrastructure team is to ensure the company can manage data at scale efficiently and seamlessly through robust and reliable data infrastructure. As a tech lead, you are required to independently lead the design, development, and optimization of our data infrastructure, collaborating closely with software engineers, data scientists, data engineers, and other key stakeholders. You are expected to own critical initiatives, influence architectural decisions, and mentor engineers to foster a high-performing team.
You will:
Lead the design and development of scalable, reliable, and secure data storage, processing, and access systems.
Define and drive best practices for CI/CD processes, ensuring seamless deployment and automation of data services.
Oversee and optimize our machine learning platform for training, releasing, serving, and monitoring models in production.
Own and develop the company-wide LLM infrastructure, enabling teams to efficiently build and deploy projects leveraging LLM capabilities.
Own the company's feature store, ensuring high-quality, reusable, and consistent features for ML and analytics use cases.
Architect and implement real-time event processing and data enrichment solutions, empowering teams with high-quality, real-time insights.
Partner with cross-functional teams to integrate data and machine learning models into products and services.
Ensure that our data systems are compliant with the data governance requirements of our customers and industry best practices.
Mentor and guide engineers, fostering a culture of innovation, knowledge sharing, and continuous improvement.

We are seeking a hands-on Tech Lead to be the first technical hire for our newly formed ML team. This is a unique ground-floor opportunity to build something from scratch and have direct influence on the technical direction of our ML capabilities. This role combines deep technical expertise with foundational leadership, requiring someone who can architect solutions, establish technical standards, and lay the groundwork for future team growth. As our founding ML tech lead, you'll have the opportunity to shape our technology choices, define our technical practices, and build the fundamental skill set that will guide the team as we scale.

What youll do: 
Shape the technical foundation: As our first ML hands on hire, choose and establish the team's core technology stack, frameworks, and development practices together with the ML leader.
Build from the ground up: Design and implement our initial ML infrastructure, data pipelines, and model deployment frameworks.
Define technical standards: Establish coding standards, best practices, and processes that will guide future team members.
Hands-on development: Lead by example through direct development and deployment of ML models and data science solutions.
Partner with stakeholders: Work closely with business teams to identify initial ML opportunities and build foundational proof-of-concept models.
Establish MLOps practices: Set up our model development, testing, deployment, and monitoring workflows from scratch.
Recruit and onboard: As the team grows, play a key role in technical interviews and help onboard new ML engineers and data scientists.
Create team culture: Help establish the team's technical culture, learning practices, and collaboration methods.
Technology evaluation: Research and select the tools, platforms, and technologies that will form our ML tech stack.
Document and share knowledge: Create foundational documentation and knowledge-sharing practices for the growing team.

Location: Tel Aviv, Israel.

Hybrid.

Full-time.