דרושים » AI » מפתח /ת למערכת הפקת תוכן תלת מימדי (TVerse)

Come join the team as a Staff Machine Learning Engineer.
We are seeking a highly skilled ML engineer passionate about building a world-class platform at a high scale, specifically focused on delivering AI capabilities.
You will be part of a vibrant team of AI Scientists and ML Engineers, helping to build the next generation of awesome products and experiences using cutting-edge Generative AI technology.
If you love having stretch goals, challenges, and making customers incredibly happy while fostering your obsessive need for perfect code and user experience, this is the job for you.
Responsibilities:
Design, implement, and enhance services at large scale, specifically focusing on improving Generative AI inference, quantization, optimization, finetuning, and evaluation.
Use your coding expertise to design and implement scalable, modular, and secure services.
Develop backend systems that support serving of LLMs and AI Agents at scale, utilizing the latest industry tools and techniques.
Work cross-functionally with product managers, AI scientists, business units, and other engineers to understand, implement, refine, and design Generative AI models.
ng end-to-end responsibilities including technical documentation and automation tests.
Interact with a variety of data sources, working closely with peers to refine features from the underlying data and build end-to-end pipelines.
Resolve defects and bugs during testing, production, and post-release patches, and participate in peer code reviews.
Explore the state-of-the-art technologies and apply them to deliver customer benefits.

We are building the next generation of AI-powered investigator tools . Our cloud-based AI platform delivers cutting-edge capabilities including multimodal analysis, embeddings, OCR, speech-to-text, face detection, image and text classification, and LLM completions through a scalable serverless architecture.
As a Senior Software Engineer on the AI Platform team, you will be part of the core team that designs, builds, maintains, and evolves the shared AI service platform that powers real investigative workflows at enterprise scale. You will help turn advanced AI concepts into production-grade serverless systems, shaping orchestration, retrieval, context, and backend architecture across the platform.

Responsibilities
Take full end-to-end ownership of features - from product thinking and design through development, testing, deployment, and post-production monitoring.
Collaborate with cross-functional teams to design, develop, and deploy high-quality web applications and services.
Champion and apply agentic development practices, leveraging AI-assisted tooling and automation to accelerate R&D velocity.
Develop robust and scalable systems using Node.js,C#,React, TypeScript and related frameworks.
Own the full DevOps lifecycle - CI/CD pipelines, containerization, release management, and production observability.
Optimize web applications for performance, scalability, and security across both microservice and monolithic architectures.
Write and maintain automated tests (unit, integration, end-to-end) to ensure code quality and reliability.
Utilize version control systems, particularly Git, for code management and collaboration.
Stay current with emerging technologies, AI tooling, and modern engineering trend.

A senior individual-contributor role reporting to the VP of MIS (CFO Office).




Key Responsibilities
The role spans two connected layers, and youll move fluidly between them:

The platform. Build and maintain the shared integration and automation infrastructure layer that connects our internal systems and exposes them to AI safely - reusable connectors, reliability, security, and observability.

The solutions. Build internal tools, automations, and AI agents on top of it - integrations across enterprise SaaS (Salesforce, NetSuite, Slack, Okta), and LLM-powered workflows for things like triage, provisioning, document processing, and reporting.

The right tool for the job. Low-code/iPaaS, custom code, or a hybrid - whatever delivers value fastest and stays maintainable, with responsible-AI practices throughout.

How Youll Work
Deliver MVP-first and iterate on real usage; favor pragmatic, simplicity-first solutions over over-engineering

Own what you build end to end - design through production support - with documentation others can maintain

Communicate trade-offs clearly, and quantify the value you deliver

Scope
In scope: the internal AI-integration and automation platform and everything built on it; business infrastructure for teams across Finance, HR, GTM, Security, and IT; integration reliability and observability.

Out of scope: physical IT operations (device provisioning, inventory, AV) and endpoint/office IT support.

Are you a senior AI researcher who thrives at the intersection of cutting-edge computer vision and real-world impact? Do you combine deep research expertise with the engineering instincts to ship models that work in production? Join the Content team within our AI group, where youll lead applied research initiatives that transform how sports content is created and experienced.

This is an applied research role with a strong ownership mandate - youll drive the full research lifecycle from problem framing and experimental design through to production integration.

We are dedicated to revolutionizing sports production. Our AI-powered solutions automatically capture sports events and transform them into personalized content, turning key moments into engaging experiences for players, teams, and fans. As a Senior AI Research Scientist, you will have direct impact on the companys innovation roadmap, leading core AI capabilities that power our platform at scale.

As part of your role, you will:
Own the research lifecycle end-to-end: problem definition, literature review, experimental design, model development, evaluation, and production integration.
Design, implement, and evaluate state-of-the-art deep learning models for computer vision tasks including detection, tracking, event recognition, and temporal modeling in video.
Drive applied research projects with clear product impact - not just experimentation, but shipping models that run reliably in production.
Collaborate closely with ML engineers, infrastructure engineers, and QA to ensure smooth model integration and performance in real-world conditions.
Contribute to our research culture: present findings internally, propose new research directions, and stay at the forefront of computer vision and deep learning.
Influence architectural decisions around model design, data pipelines, and evaluation frameworks.