Machine Learning Engineer, Training Infrastructure

Hedra•San Francisco, CA

10h•Onsite

About The Position

About Hedra Hedra is a pioneering generative media company backed by top investors at Index, A16Z, and Abstract Ventures. We're building Hedra Studio, a multimodal creation platform capable of control, emotion, and creative intelligence. At the core of Hedra Studio is our Character-3 foundation model, the first omnimodal model in production. Character-3 jointly reasons across image, text, and audio for more intelligent video generation — it’s the next evolution of AI-driven content creation. At Hedra, we’re a team of hardworking, passionate individuals seeking to transform content creation and build a generational company from the ground up. We value startup energy, initiative, and the ability to turn bold ideas into real products. Our team is fully in-person in SF/NY with a shared love for whiteboard problem-solving. Overview We are looking for an ML Engineer with 3+ YOE in high-performance computing systems to manage and optimize our computational infrastructure for training and deploying our machine learning models. The ideal candidate has diverse experience managing ML workloads at scale, supporting our 3DVAE and video diffusion models. This role is vital for ensuring the computational backbone supports the company’s ML efforts, focusing on deployment and scalability.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field, with a focus on system administration.
Experience with cloud computing platforms such as Amazon Web Services, Google Cloud, or Microsoft Azure, essential for managing large-scale ML workloads.
Values engineering processes and version control (CI/CD).
Knowledge of containerization technologies like Docker and Kubernetes is required for deployments at scale.
Understanding of distributed training techniques and how to scale models across multi-node clusters, aligning with video generation needs.
Strong problem-solving and communication skills, given the need to collaborate with diverse teams.

Responsibilities

Design, implement, and maintain scalable computing solutions for training and deploying ML models, ensuring infrastructure can handle large video datasets.
Manage and optimize the performance of our computing clusters or cloud instances, such as AWS or Google Cloud, to support distributed training.
Ensure that our infrastructure can handle the resource-intensive tasks associated with training large generative models.
Monitor system performance and implement improvements to maximize efficiency and utilization, using tools like Airflow for orchestration.
Collaborate across research teams to understand their computational needs and provide appropriate solutions, facilitating seamless model deployment.