we are a game-changing startup that's giving advanced electronics the power to report on their own health. In a digital world built for autonomous driving, cloud computing, and AI, we depend on computing systems daily. But how can we guarantee their safety, reliability and functionality? we are the first-ever company to provide visibility into next-gen chips while they are operating, based on the power of on-chip monitoring, machine learning, and data analytics.
Here at our company, you'll be part of a team that's unlocking deep insights to make electronics more reliable, efficient, and high-quality. We're trusted by industry leaders in data centers, automotive, communications, and consumer devices - we work with the world's largest and most notable companies in tech.
We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.

Required Research Scientist - LTX Model Quality
The role
Following the success of LTX-2, our widely adopted open-source text-to-audio+video model, we are expanding our efforts to develop cutting-edge audio+video generation models and are hiring Research Scientists to join our LTX-Applications team.
As a Research Scientist in the LTX Model Quality team, you will play a key role in elevating the quality, controllability, and alignment of our video generation model. This role focuses on the critical post-training phase-developing and implementing techniques such as preference optimization, reward modeling, and human feedback integration to refine model outputs. You will design robust evaluation frameworks, define quality metrics, and build systematic approaches to identify and address model failure modes. Your work will directly impact the quality of the videos we generate.
What you will be doing
Develop and implement post-training pipelines, including RLHF, DPO, and other preference-based optimization techniques for video generation models.
Fine-tune and control VLLMs for video and audio understanding.
Design and iterate on quality evaluation metrics and frameworks.
Conduct systematic failure mode analysis and develop targeted interventions to address quality gaps.
Build and curate high-quality preference datasets and evaluation benchmarks that capture nuanced aspects of video generation quality.
Collaborate closely with fellow researchers to establish tight feedback loops between human judgment and model improvement.

we are a game-changing startup that's giving advanced electronics the power to report on their own health. In a digital world built for autonomous driving, cloud computing, and AI, we depend on computing systems daily. But how can we guarantee their safety, reliability and functionality? we are the first-ever company to provide visibility into next-gen chips while they are operating, based on the power of on-chip monitoring, machine learning, and data analytics.
Here at our company, you'll be part of a team that's unlocking deep insights to make electronics more reliable, efficient, and high-quality. We're trusted by industry leaders in data centers, automotive, communications, and consumer devices - we work with the world's largest and most notable companies in tech.
We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.

We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.

We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.