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Job Description:
Work within the finance department as part of the accounting team.
Manage supplier and/or customer accounts.
Perform bank reconciliations and issue invoices.
Handle payments to suppliers and various additional tasks.

Job Description:
Warehouse operations within the logistics center of a global high-tech company.
Responsibility for all stages of warehouse processes.
Work related to export operations.
Work in a physically demanding environment.

We are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers our data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.

This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make our data and AI platforms scalable, reproducible, secure, compliant, and easier to use.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Architectural Leadership: Own and evolve the technical roadmap for our data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.

we are looking for a AI consultant.
As an AI Consultant and a CS team member, you will work at the technological forefront of the data analytics world by helping customers adopt and realize business value through AI and Machine Learning. As a CSM you will be the customers key strategic advisor and implementer and you will work with senior stakeholders to:
Identify AI opportunities
Frame and size the value and the business impact AI can help customers realize
Drive the implementation of software and services by collaborating and managing a cross-functional account team
Ongoing account management and commercial success including value realization analysis, Quarterly Business Reviews (QBRs), Pilot process management, etc.,
Gradually develop business-ML expertise and lend this SME towards customers and our internal product development

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our 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.

This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.

Location: Ramat Gan, Israel (hybrid model)

What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our 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.

we are looking for a Senior ML Engineer.
Responsibilities:
Work on core data and machine learning infrastructure, which is at the heart of offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.

we are looking for a Customer-Facing Data Scientist.
As a key Customer-Facing Data Scientist on the Demand Forecasting team, you will:
Work at the forefront of the data world, focusing on the optimization and deployment of Demand Forecasting models.
Handle complex data science challenges using state-of-the-art techniques relevant to demand forecasting and time series, such as forecasting model ensembling, anomaly detection, feature engineering for trend/seasonality, and causal inference modeling.
Act as a highly communicative partner to our clients, diving deep into their unique datasets to understand the nuances and challenges of their business.
Lead the rigorous testing and application of our core ML pipeline on new customer data, identifying areas where it performs exceptionally well and researching/developing solutions for complex cases where it does not.
Be instrumental in helping our clients achieve their business goals and realize the measurable value of Demand Forecasting module.
Work within a production and product-oriented environment, ensuring models are robust, scalable, and directly integrated into the clients operational workflow.

As a Senior DevOps Engineer, you will join our data & engineering team and design and manage scalable, cloud-based infrastructure focused on big data and machine learning. You will ensure high-scale production environments are reliable, secure, and efficient, supporting seamless ML/AI deployments.

Location: Ramat Gan, Israel (hybrid model)

Reporting to: DevOps Team Lead

What will you do?
Design, build, and maintain scalable and reliable infrastructure for our big data applications.
Automate the deployment and scaling of our applications using Kubernetes and related technologies.
Set up, operate, and maintain Kubernetes-based environments, including GKE clusters and Helm-based deployments.
Support deployment workflows for PyTorch and TensorFlow-based ML/AI workloads.
Support and optimize GPU cluster infrastructure for machine learning workloads.
Build and maintain CI/CD pipelines using tools such as GitHub Actions.
Set up and maintain deployment environments for ML services and infrastructure components.
Work with MLOps platforms, including ClearML, to support experiment tracking, orchestration, and production workflows.
Manage cloud infrastructure on GCP with attention to reliability, scalability, cost awareness, and security.
Build and maintain cloud networking and security configurations.
Implement observability and monitoring using tools such as Grafana, Loki, and Prometheus.
Collaborate with DevOps, ML, data, and engineering teams to improve platform reliability and developer experience.