Sieve - Applied Research Engineer

Location:

San Francisco, USA (on-site)

Type: Full-time
Salary: $200,000–$250,000 + 0.05%–0.4% equity

Client is the only AI research lab exclusively focused on video data
. They combine exabyte-scale video infrastructure
, novel video understanding techniques, and dozens of data sources to create high-quality datasets for top AI labs. The company raised Series A from Matrix Partners, Swift Ventures, Y Combinator, AI Grant and has 12 employees.

As an Applied Research Engineer, you will:

• Build high-performance building blocks
  and large-scale pipelines to process and understand video data with precision at internet scale.
• Tackle ambiguous research problems, optimize ML models and APIs, and create end-to-end solutions in computer vision, audio, and text processing domains.
• Work with models and APIs, optimizing performance through pre/post-processing, parallelism, pipelining, inference optimization, and occasional fine‑tuning.
• This role is ideal for engineers who enjoy both research and production engineering
  , want to push the frontier
  of video understanding
  , and can independently own full pipelines from research to deployment.

• Develop large-scale video processing pipelines with high precision.
• Optimize ML models and APIs using pre/post-processing, parallelism, pipelining, and inference tuning.
• Collaborate with customers and external teams to define requirements and iterate on solutions.
• Build clean, maintainable code that forms the foundation for research and production systems.
• Contribute to end-to-end products, not just individual models.

• Minimum of 4+ years of experience in computer vision or audio processing.
• Strong Python skills and hands‑on experience with PyTorch or similar ML frameworks.
• Excellent communication skills, especially with external stakeholders.
• Motivation to work end-to-end on products.
• Ability to break problems down from customer impact to technical building blocks.
• Willingness to work in‑person at the San Francisco HQ.

• Active contributor to open‑source projects.
• Experience as an early hire at a startup.