Machine Learning Engineer (CUDA) - Hedra : Job Details

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. Note: At Hedra, we're a team of hard-working, passionate individuals seeking to fundamentally change content and build a generational company together. You should have start-up experience and be a self-starter that is driven to build impactful products that change the status quo. The Hedra team is fully in-person in SF/NY with a shared love of solving hard problems using whiteboards. Overview: We are seeking a talented CUDA ML Engineer to optimize our machine learning models for high-performance computing on GPU hardware. The ideal candidate will have expertise in CUDA programming and a deep understanding of how to leverage GPU acceleration to maximize the efficiency of our 3DVAE and video diffusion models. Responsibilities:

• Optimize machine learning models, specifically 3DVAE and video diffusion models, for GPU performance using CUDA, ensuring efficient training and inference.
• Develop and implement efficient algorithms and data structures for GPU computation, addressing performance bottlenecks in video generation tasks.
• Work closely with the research and engineering teams to understand model requirements and performance bottlenecks, facilitating collaboration.
• Stay current with the latest advancements in GPU technology and machine learning optimization techniques.
• Ensure that our models run efficiently on various GPU architectures, supporting scalability for large-scale training.

Qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, or a related field, with a focus on high-performance computing.
• Strong programming skills in C++ and CUDA, essential for GPU optimization.
• Experience with deep learning frameworks that support GPU acceleration, such as PyTorch or TensorFlow, crucial for model implementation.
• Understanding of parallel computing concepts and GPU architecture, given the need to optimize for hardware constraints.
• Familiarity with machine learning models, particularly generative models, to align optimizations with model needs.
• Excellent problem-solving and debugging skills, necessary for addressing performance issues.

Benefits:

• Competitive compensation and equity
• 401k (no match)
• Healthcare (Silver PPO Medical, Vision, Dental)
• Lunch and snacks at the office

We encourage you to apply even if you don't fully meet all the listed requirements; we value potential and diverse perspectives, and your unique skills could be a great asset to our team.