Pika

ML Engineer, Inference & Optimization

2w ago
185000 –350000 USD / yearUSASenior
Pika

ML Engineer, Inference & Optimization

2w ago
185000 –350000 USD / yearUSASeniorcudancclgpu parallelisminference accelerationquantizationmodel serving

Lead and optimize inference acceleration and GPU parallelism to improve performance of AI-driven video and language models.

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.
  • 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.

Nice to have

  • 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

Conditions

  • Competitive salary in the AI industry
  • Equity in a fast-growing startup shaping the future of AI
  • Comprehensive health benefits, monthly stipends, company retreats
  • A supportive and collaborative office culture—we’re all building and launching together

Other

  • 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.
  • 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.
  • At Pika, we're crafting a future where video creation is seamless, intuitive, and universally accessible. Our mission is to empower creativity by breaking down technical barriers using the transformative power of AI. We’re a tight-knit, energetic team based in Palo Alto, CA, valuing efficiency, curiosity, and the ambition to make a meaningful impact on the world.
  • We work from our Palo Alto office 3–5 days a week and welcome applicants who are eager to contribute onsite.