דרושים » תוכנה » Senior Machine Learning Engineer

We are looking for an experienced Machine Learning Engineer to build and scale production-grade ML solutions, primarily on large-scale tabular automotive data for quality and cybersecurity products. Youll help define the engineering standards that enable the Data Science team to deliver at scale, with opportunities to own selected ML features end-to-end.
Youll work with massive (often live-streaming) automotive datasets and real-world constraints around latency, CPU, memory, and I/O. This is a highly collaborative role, and youll partner closely with data science and data engineering teams. Youll be driving technical alignment through clear communication and mentoring others via design and code reviews.
This role is full-time and based in Herzliya, Israel.
Responsibilities
Act as the DS engineering axis: drive designs with focus on performance (I/O, CPU, memory, latency, cost).
Lead heavy ML engineering efforts when needed (optimization, scaling, reliability), while collaborating with other team members and supporting them in their ML-related projects.
Own selected ML features and projects end-to-end, including DS work (EDA, features, modeling, evaluation) plus production delivery, monitoring and iteration.
Build production tabular - ML components: training, batch/near-real-time scoring, inference services, and shared libraries.
Set standards and tooling for profiling and preventing performance regressions.
Partner with the data engineers on data / ML contracts (schemas, SLAs, formats/partitioning) between pipelines and ML components.
Raise the bar via mentoring, documentation, and a strong code/design review culture, in the Data Science team.

Required Senior Data Engineer
Join our core platform engineering team, developing our AI-powered automotive data management platform.
We are developing the next generation data-driven products for the Automotive industry, focusing on cybersecurity (XDR) and vehicle quality. Our products monitor and secure millions of vehicles worldwide and help automakers leverage connected vehicle data to deliver cyber resilience, safety, customer satisfaction and increase brand loyalty.
Our Data Engineering & Data Science Group leads the development of our Iceberg-based data platform, including data lake, query engine, and ML-Ops tools, serving as a solid AI-ready foundation for all our products.
At the core of our Engineering Team, you will build and operate scalable, production-grade customer-facing data and ML platform components, focusing on reliability and performance.
Technological background and focus: Iceberg, Trino, Prefect, GitHub Actions, Kubernetes, JupyterHub, MLflow, dbt
This role is full-time and is Herzliya, Israel based.
Responsibilities
Design, build, and maintain scalable data pipelines to ingest and transform batch data on our data lake, enabling analytics and ML with strong data quality, governance, observability, and CI/CD.
Build and expand our foundational data infrastructure, including our data lake, analytics engine, and batch processing frameworks.
Create robust infrastructure to enable automated pipelines that will ingest and process data into our analytical platforms, leveraging open-source, cloud-agnostic frameworks and toolsets.
Develop and maintain our data lake layouts and architectures for efficient data access and advanced analytics.
Develop and manage orchestration tools, governance tools, data discovery tools, and more.
Work with other team members of the engineering group, including data scientists, data architects, and data analysts, to provide solutions using a use case-based approach that drives the construction of technical data flows.

At our company, we move fast. Were an ultra-collaborative company with brilliant people who care deeply about the details. Together, were solving interesting and complex puzzles to keep the worlds data safe.
We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.
Responsibilities
Design and Build ML Infrastructure: Develop and maintain scalable, production-ready infrastructure for both traditional ML (anomaly detection, user behavior analytics) and LLMs across enterprise environments.
Optimize Model Performance: Analyze and optimize LLM and ML performance using techniques like knowledge distillation, quantization, and efficient data structures to boost efficiency and lower resource costs.
Deploy and Integrate: Collaborate heavily with software and data engineers to integrate models into production pipelines, cloud-native environments, and on-premises workflows.
Drive MLOps & Tooling: Manage the complete model lifecycle (monitoring, retraining, deployment) and actively build custom tools from scratch to improve the team's ML workflows.
Elevate Engineering Standards: Perform rigorous code reviews, ensure robust Python production standards, and provide technical guidance to data scientists and junior engineers.
Cross-Functional Partnership: Partner with cybersecurity researchers and product teams to translate research insights and threat analysis features into highly performant production code.
Open-Source Engagement: Actively engage with the open-source community by contributing code and expertise to relevant ML/LLM projects.

Join us at our company, a global fintech leader (NASDAQ; TASE: NYAX) revolutionizing the world of cashless payments, consumer engagement, and business management solutions. With more than 1,200 employees across 12 offices worldwide. At our company, youll be part of a diverse and innovative community where your work makes a real impact and helps shape the future of payments.
Syte is an image recognition solution for retailers in different verticals such as fashion, home, furniture, DIY etc. We offer AI-powered product recommendation and discovery platform. Our solutions include visual search, automated product tagging, advanced personalized recommendations, and more.
We are currently looking for a Senior Machine Learning Engineer to design and implement scalable and integrate machine learning solutions into production. You will work with modern technologies like Node.js, Python, Kafka, MongoDB, and OpenSearch, AirFlow, while driving architectural decisions and CI/CD best practices. This role combines backend engineering excellence with ML deployment expertise.
The Senior Machine Learning Engineer will report directly to the Director of R&D.
Your key responsibilities will include:
Architect and develop MLE services using Python, ensuring high performance, reliability, and scalability.
Collaborate with Data Science teams to productionize ML models (model serving, monitoring, retraining pipelines).
Build and maintain CI/CD pipelines for automated testing, deployment, and monitoring.
Design and implement system architecture for distributed, event-driven systems.
Integrate and optimize Kafka for real-time data streaming and event processing.
Design and manage MongoDB schemas and queries for optimal performance.
Implement and maintain OpenSearch clusters for search and analytics use cases.
Ensure security, observability, and fault tolerance across all services.
Mentor team members and contribute to engineering best practices.

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.