ML Engineer, Inference & Optimization

Palo Alto HQfull timeOn Site

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mid

$250k–$350k

H1B: no

Pika Senior/Staff level Inference Engineer role focused on accelerating AI-driven product performance through inference optimization and GPU parallelism. Responsible for designing and optimizing inference pipelines and collaborating with research and engineering teams on video and language model deployment.

Responsibilities

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.
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.

Qualification

ExperienceInference MasteryGPU & ParallelismAI Domain KnowledgeCollaborationOwnership MindsetBonusStartup or rapid prototyping experience

Required

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.

Preferred

Experience with high-throughput video or real-time streaming model deployment
Familiarity with distributed training and optimization toolkits
Contributions to open source projects in AI infrastructure or deep learning compilers
Startup or rapid prototyping experience