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Machine Learning Engineer (CUDA)

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. You must be willing to work in-person in either NYC or SF.

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.

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Average salary estimate

$125000 / YEARLY (est.)
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$100000K
$150000K

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What You Should Know About Machine Learning Engineer (CUDA), Hedra

Join Hedra as a Machine Learning Engineer (CUDA) in San Francisco, where we are at the forefront of generative media innovation. Backed by top investors like Index and A16Z, our mission is to redefine content creation through our groundbreaking Hedra Studio platform. As part of our passionate team, you will optimize cutting-edge machine learning models, specifically our 3DVAE and video diffusion models, harnessing the power of GPU hardware. Your primary responsibility will be to ensure our models perform efficiently by implementing CUDA programming for high-performance computing. Collaborating closely with research and engineering teams, you'll tackle performance bottlenecks and stay updated on the latest in GPU technology. We're looking for a candidate who has a background in high-performance computing and is proficient in C++ and CUDA, along with experience in deep learning frameworks like PyTorch or TensorFlow. At Hedra, we value self-starters who are ready to make an impact, and we appreciate diverse backgrounds and unique skills. Our workplace offers competitive compensation, benefits like healthcare, and a supportive environment for growth. If you're eager to push the boundaries of AI-driven content creation, we'd love to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer (CUDA) Role at Hedra
What are the responsibilities of the Machine Learning Engineer (CUDA) at Hedra?

As a Machine Learning Engineer (CUDA) at Hedra, you will be responsible for optimizing our machine learning models, particularly the 3DVAE and video diffusion models, for high-performance GPU computing. This entails developing and implementing algorithms that address performance bottlenecks, collaborating with various teams to understand model requirements, and staying up-to-date with developments in GPU technology. Your work will ensure our models operate efficiently on multiple GPU architectures, which is crucial for scalability in training and inference.

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What qualifications do I need to be a Machine Learning Engineer (CUDA) at Hedra?

To qualify for the Machine Learning Engineer (CUDA) role at Hedra, you should possess a Bachelor’s degree in Computer Science, Electrical Engineering, or a related field with a focus on high-performance computing. Essential skills include strong programming competencies in C++ and CUDA, along with experience in deep learning frameworks such as PyTorch or TensorFlow. Familiarity with generative models and parallel computing concepts will also greatly benefit your application.

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How does the work environment look for a Machine Learning Engineer (CUDA) at Hedra?

The work environment at Hedra is dynamic and collaborative. You'll be part of a passionate team dedicated to transforming content creation using innovative technology. We work closely on cutting-edge projects in a startup-like setting, where creativity and problem-solving are encouraged. Our San Francisco office offers a comfortable space for collaboration, as well as thoughtful perks like lunch and snacks to fuel your workday.

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Is experience in startup environments important for the Machine Learning Engineer (CUDA) position at Hedra?

Yes, experience in startup environments is highly valued for the Machine Learning Engineer (CUDA) position at Hedra. We are looking for candidates who are self-starters and are comfortable navigating the fast-paced and evolving challenges typical of startups. Your adaptability and drive will be essential in developing impactful products that push the boundaries of AI-driven content creation.

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What benefits does Hedra offer to Machine Learning Engineers (CUDA)?

Hedra offers a competitive compensation package for Machine Learning Engineers (CUDA), including equity options and a 401k plan. Additionally, we provide comprehensive healthcare benefits, including Silver PPO Medical, Vision, and Dental coverage. Our office also supports a positive work culture, complete with lunch and snacks to keep our team energized. We are committed to fostering an inclusive workplace where diverse perspectives are welcomed.

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Common Interview Questions for Machine Learning Engineer (CUDA)
Can you explain your experience with CUDA and how it relates to GPU optimization?

When answering this question, highlight your hands-on experience with CUDA programming and specific projects where you optimized machine learning models for GPU performance. Discuss techniques you used to enhance efficiency, such as memory management and kernel optimization, and feel free to include outcomes that showcase your success in these endeavors.

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What strategies do you employ to diagnose performance bottlenecks in machine learning models?

You should elaborate on various diagnostic strategies you’ve utilized, such as profiling tools and benchmarking tests. Discuss specific metrics you analyze to identify issues, and talk about your approach to addressing these bottlenecks, including both code optimization and architectural adjustments.

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How do you stay updated with the latest advancements in GPU technology and machine learning optimization?

Be prepared to share your methods for staying current, such as following reputable AI research journals, attending industry conferences, or participating in online forums. Mention any specific thought leaders or resources that you find particularly informative and how you implement new findings into your projects.

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Describe a successful project where you utilized deep learning frameworks like PyTorch or TensorFlow.

Use this opportunity to narrate a specific project, outlining your role, the challenges faced, and how you leveraged deep learning frameworks to achieve the desired result. Be specific about the models used, data handling techniques, and performance improvements your efforts led to.

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What generative models are you familiar with, and how would you approach optimizing them?

Talk about generative models you have worked with, explaining their architecture and functionality. Highlight insights into how you would optimize these models for performance on GPU, including strategies such as loss function tuning and data normalization adjustments.

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How would you approach working with a cross-functional team to optimize a machine learning model?

Illustrate your collaborative approach and communication style in cross-functional teams. Discuss how you ensure alignment on project goals and facilitate knowledge sharing, while also outlining how you gather model requirements and performance feedback effectively from different stakeholders.

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Can you provide an example of a performance issue you've faced with GPU computing and how you resolved it?

Share a real-world example of a specific performance issue you tackled in GPU computing. Explain the context, the steps you took to diagnose the issue, and the ultimate solution you implemented, along with any lessons learned that would benefit future projects.

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What is your experience with parallel computing concepts, and why are they important for this role?

Discuss your foundational understanding of parallel computing concepts, emphasizing how they apply specifically to high-performance machine learning tasks. Highlight instances where you implemented parallel processing to enhance model training or inference times.

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How do you prioritize your tasks when managing multiple optimization projects simultaneously?

Outline your time management strategies and tools for prioritizing work. Emphasize your ability to assess project impact and urgency while maintaining clear communication with team members to ensure all optimization projects progress smoothly.

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Why do you want to work as a Machine Learning Engineer (CUDA) at Hedra?

Convey your enthusiasm for joining Hedra by connecting your personal career aspirations to the company’s vision and mission. Highlight specific aspects of Hedra’s culture, projects, or technology that resonate with your professional values and how you envision contributing to the team.

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Full-time, on-site
DATE POSTED
March 13, 2025

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