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We are seeking a Senior Engineer for our Labs (on premise) Team member, working with a team of engineers, who manage our physical labs. You'll support the infrastructure for our servers, both HW and SW wise. This role offers an exciting opportunity to take a significant part of ensuring optimal testing environments for our enterprise-grade storage solutions.
Requirements:
- Hands-on work in a data center environment: racking, cabling, replacing hardware components, and troubleshooting server failures
- Design and implement infrastructure to allocate, configure and troubleshoot servers
- Daily operational support for servers, switches, and lab infrastructure
- Building and maintaining Linux images for servers and lab environments
- Working with a mix of modern and legacy systems in a lab environment
- Supporting VMware-based infrastructure and virtualization environments
- Work with multiple teams on gathering requirements and coordinating changes
- Define and implement infrastructure monitoring strategies using modern observability tools
- Drive infrastructure modernization initiatives and best practices adoption
- Establish and maintain documentation for lab processes, infrastructure, and operational procedures
- Perform capacity planning and resource allocation for lab environments
- Working closely with procurement, vendors, and external technicians
- Participating in on-call rotation (small team, occasional incidents)
Occasional travel to the US (1-2 times a year).

Required Performance Engineer
About the role;
Were building a next-generation platform for validating large-scale distributed systems. Our goal is to continuously ensure the correctness, performance, and resilience of the Data Platform across every layer of the stack.
As a Senior Software Engineer, youll work hands-on on the systems and frameworks that test, stress, and validate complex distributed infrastructure under real-world conditions. Youll help design and build automated environments that simulate scale, concurrency, and failure scenarios, and youll contribute to evolving how we ensure reliability and correctness in modern infrastructure systems.
This role is ideal for engineers with a strong distributed systems background who enjoy deep technical problem-solving, working close to the system, and building tools that improve quality, stability, and confidence at scale.
What Youll Do:
Design and implement core components of a distributed testing infrastructure and quality platform.
Build automated frameworks to validate functionality, performance, and resilience at scale.
Collaborate closely with infrastructure, storage, and platform teams to ensure quality is built into the development lifecycle.
Contribute to improving tooling, test coverage, and engineering best practices across the organization.

We're looking for a Senior Talent Acquisition Partner - R&D who's genuinely curious about AI and technology, knows how to earn the trust of technical leaders, navigates ambiguity with confidence, and pushes hiring processes forward.
What you will be doing:
Own the full recruiting lifecycle for global R&D roles across Engineering, Research, Data, and Product teams in Israel, the UK, and the US.
Partner closely with R&D leadership and hiring managers to define hiring strategy, calibrate talent profiles, and advise on market realities.
Lead proactive sourcing strategies to identify and engage exceptional passive talent, with a strong emphasis on the AI/ML, deep learning, and generative AI ecosystem.
Drive structured, fast-moving hiring processes with clear stakeholder communication, sharp prioritization, and zero drop-off in candidate care.
Leverage AI-powered tools to build smarter, more efficient recruiting workflows.
Identify friction points in the hiring process and lead the implementation of solutions - treating recruitment operations with the same rigor you'd expect from an engineering team.
Partner with the HR team to drive initiatives that strengthen manager capabilities, elevate the candidate journey, and contribute to an exceptional employee experience across the organization.

Required Research Scientist - Audiovisual Understanding, Model Foundations
Team & role
The Core Generative AI team is a unified group of researchers and engineers dedicated to developing our generative foundational models that serve LTX Studio, our AI-based video creation platform. Our focus is on creating a controllable, cutting-edge video generative model by merging cutting-edge algorithms with exceptional engineering. This involves enhancing machine learning components within our sophisticated internal training framework, crucial for developing advanced models. We specialize in both research and engineering that enable efficient and scalable training and inference, allowing us to deliver state-of-the-art AI-generated video models.
As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.
What you will be doing:
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.