ML Engineer, Inference & Optimization

About the role

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 and engineering functions.

- Ownership Mindset: Self-driven, solutions-oriented, and capable of managing ambiguity in a fast-paced startup environment.

- Bonus: Experience in enhancing training efficiency, stability, or resource optimization for large models.

NICE TO HAVE

- Experience with high-throughput video or real-time streaming model deployment

- Familiarity with distributed training and optimization toolkits