דרושים » הנדסה » Large Scale Training Engineer - LTX Model

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads

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.

The Model Evaluation team is the central nervous system of the LTX Foundation Model group. We don't just measure performance; we define what "good" looks like across a vast array of use cases. While we power the next generation of creative tools, LTX is also a foundational engine for simulation pipelines, game engines, synthetic data generation, architectural rendering, and digital avatars. We act as the critical bridge between raw research and industrial-grade reliability, building the benchmarks that ensure our models are world-class for both artists and engineers.

The Role
As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.

Key Responsibilities
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.

This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.

What you will be doing
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.

We are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.

What you will be doing
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.

we are looking for a AI Security Engineer.
As AI capabilities accelerate across the bank, we need an engineer to design and enforce safe AI usage-protecting customer data, preserving model integrity, and meeting our regulatory obligations. You'll be the architect of guardrails, tooling, and policies that make AI both secure and useful for product and internal teams. This isn't about slowing things down; it's about building the trust layer that lets innovation move fast without breaking things.
What Youll Actually Be Doing:
Design enterprise AI guardrails across Azure and AWS (e.g., Azure AI Studio/Azure OpenAI, Amazon Bedrock/SageMaker): content filtering, PII redaction, prompt/response validation, and policy enforcement services.
Implement data minimization controls for GenAI/RAG workloads: context filtering, least‐privileged retrieval, document-level ACL enforcement, vector store hardening, and secure token/secret handling.
Threat model AI systems (apps, agents, RAG, fine-tuning pipelines) using frameworks like STRIDE and the OWASP Top 10 for LLM Apps; define misuse scenarios (prompt injection/jailbreaks/data exfiltration) and build mitigations.
Build monitoring and telemetry: privacy-preserving prompt/response logging, sensitive-data detection, safety/eval dashboards, drift/abuse signals, and incident hooks into our SIEM.
Integrate AI security into the SDLC: reusable libraries, pre-commit checks, CI/CD gates, policy-as-code, and secure-by-default reference architectures for product teams.
Evaluate third‑party AI vendors and internal apps: security reviews, data residency and retention requirements, SSO/SCIM integrations, DPA/TPRM inputs, and continuous control testing.
Partner across Security, Data, Privacy, and Engineering to map AI controls to FFIEC, SOC 2, and PCI DSS; document control evidence for audits.
Lead/participate in AI red‑teaming: automated jailbreak/prompt‑injection tests, safety benchmarks, purple‑team exercises, and response playbooks for AI incidents.
Enable the org with concise guidelines, examples, and training on safe AI development and usage.

Your experience building well-architected, high-performance frontend components will directly impact how quickly teams can ship, how consistent our products feel, and how confidently we can continue to innovate at scale.

What Youll Do:
Build and maintain shared frontend foundations used across products, with a strong focus on scalability, performance, and long-term maintainability.
Build, document, and maintain a shared UI components library used across products.
Develop high-performance data visualization components and reusable interface patterns.
Collaborate with design and product teams to transform Figma designs into functional React components.
Take ownership of projects and collaborate effectively across teams to deliver production-grade UI solutions.
Participate in the modernization and technical health of the frontend by refactoring, improving architecture and state management, enforcing code quality standards, and continuously optimizing rendering, bundling, and build workflows
Leverage AI tooling to assist with frontend development workflows, including code generation and refactoring, performance analysis and design-to-code alignment, while applying strong engineering judgment to review and productionize results.

As the ML Platform Tech Lead, youll be responsible for the development of AI infrastructure, taking an active role in mentoring backend engineers directly.

This is a hands-on tech- lead position that blends system design and technical strategy. You will be expected to work closely with our ML research teams, product, and engineering stakeholders to take cutting-edge AI models into production, powering features across our creative apps used by millions worldwide.

You will own the full development lifecycle, from system architecture through deployment and operation, while also being accountable for team delivery, collaboration, and growth.

We are looking for a backend engineer who can design, build, and operate highly reliable Node.js services on AWS that enable generative‑AI capabilities across our products and internal workflows.
You will create scalable APIs, data pipelines, and serverless architectures that integrate large‑language‑model (LLM) services such as Amazon Bedrock, OpenAI, and open‑source models, enabling teams to safely and efficiently leverage generative AI.
What Youll Actually Be Doing
Design and implement REST/GraphQL APIs in Node.js/TypeScript to serve generative‑AI features such as chat, summarization, and content generation.
Build and maintain AWS‑native architectures using Lambda, API Gateway, ECS/Fargate, DynamoDB, S3, and Step Functions.
Integrate and orchestrate LLM services (Amazon Bedrock, OpenAI, self‑hosted models) and vector databases (Amazon Aurora pgvector, Pinecone, Chroma) to power Retrieval‑Augmented Generation (RAG) pipelines.
Create secure, observable, and cost‑efficient infrastructure as code (CDK/Terraform) and automate CI/CD with GitHub Actions or AWS CodePipeline.
Implement monitoring, tracing, and logging (CloudWatch, X‑Ray, OpenTelemetry) to track latency, cost, and output quality of AI endpoints.
Collaborate with ML engineers, product managers, and front‑end teams in agile sprints; participate in design reviews and knowledge‑sharing sessions.
Establish best practices for prompt engineering, model evaluation, and data governance to ensure responsible AI usage.

An AI-first tech company in the automotive space with hubs across the US and Israel. Our mission is to constantly disrupt the industry by creating new, groundbreaking technologies to help dealers build stronger, more resilient businesses. Our work happens in the fast lane as we work to bring AI and data -driven solutions to a quickly evolving industry. Our team is made up of curious and creative individuals who are always looking to achieve the impossible. We are bold, collaborative, and goal driven, and, at our core, we believe every voice has value and can impact our bottom line. We are looking for a Top University Graduate to join us as a Junior software engineer and make a real impact on our R&D team! In this role you will have the opportunity to take ownership of the development of amazing new products and work on interesting projects inside a smart, fun, and dynamic engineering team. We seek an individual passionate about tackling tech challenges, with a proactive and AI first approach to push forward a love for coding and a dedication to excellence. This is a full-time, on-site position based in our Jerusalem office in Talpiyot or Tel-Aviv office near HaShalom Train Station.

What you will be responsible for:

* Write clean, high-quality, maintainable code across our stack
* Take ownership of features and projects end-to-end - from design through deployment
* Collaborate closely with product, design, and fellow engineers to ship impactful solutions

Why you should join us:

* An awesome global team of forward-thinking, innovative go-getters
* Learning and growth opportunities within a fast-paced tech startup environment
* Clear career advancement path for strong performers
* As a part of our team, your success in this role is crucial to our company's growth. We are committed to supporting you in achieving your KPIs and goals and providing regular feedback to help you reach your full potential. Together, we can make a real impact and achieve great things!
* Cibus and lots of yummy treats:-)