ML Engineer, Inference & Optimization

Excerpt from the original posting

About the Role
We are seeking Senior/Staff level Inference Engineers to accelerate the performance of Pika's AI-driven products. In this highly technical role, you will operate at the intersection of cutting-edge inference acceleration, GPU parallelism, advanced model deployment, and video generation technologies. Your expertise will drive significant improvements to model speed and efficiency, ensuring our creative AI systems deliver industry-leading user experiences at scale.

You will design and optimize inference pipelines, implement state-of-the-art acceleration techniques, and work closely with researchers and engineers across the team to push the boundaries of what’s possible in real-time AI deployment. Your efforts will play a foundational role in powering the next generation of Pika’s video and language models.

What You’ll Do
- Accelerate Inference : Lead and implement advanced inference acceleration techniques, including attention optimization and quantization for efficient model serving.

- Maximize GPU Parallelism : Engineer and optimize GPU strategies across tensor, sequence, and pipeline parallelism (TP, SP, PP) for maximal efficiency and scalability.

- Programming for Performance : Develop and optimize high-performance computing kernels and distributed workloads using CUDA and NCCL.

- Advance AI Deployment : Collaborate with research and engineering teams to bring state-of-the-art videogen and large language models into production.

- Improve Training Efficiency : (Bonus) Contribute to improvements in model training speed, stability, and resource utilization as part of our deployment lifecycle.

- Technical Excellence : Drive rigorous code reviews, participate in technical discussions, and mentor fellow engineers on best practices in inference and GPU programming.

What We’re Looking For
- Experience : 5+ years engineering experience, with a strong track record in inference acceleration and model deployment at scale.

- Inference Mastery : Proven expertise in inference optimization, including quantization, attention acceleration, and deep learning compiler stacks.

- GPU & Parallelism : Deep knowledge of GPU programming (CUDA, NCCL) and experience with SP, TP, PP, and other forms of parallelism for distributed inference.

- AI Domain Knowledge : Familiarity with video generation (videogen) models and large language models (LLMs).

- Collaboration : Strong cross-discipline communication skills; able to drive shared goals across research an…

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