5 months ago
Base Salary
$150k - $350k/yr
Responsibilities
- Work directly with customers and internal teams to determine required data and translate ambiguous requirements into technical systems
- Build and ship production systems that find, generate, filter, transform, evaluate, and package high-quality video datasets at scale
- Develop custom algorithms, models, workflows, and large-scale data pipelines
- Work across computer vision, audio processing, text processing, metadata analysis, model adaptation, and quality evaluation
- Improve system performance through preprocessing, post-processing, parallelism, inference optimization, fine-tuning, and evaluation loops
- Deliver reliable end-to-end production outcomes from research prototypes
Requirements
- Comfort working directly with customers or external teams to translate ambiguous needs into concrete technical systems
- Strong Python development skills and hands-on experience with PyTorch or similar machine learning frameworks
- Experience building custom algorithms, model workflows, or large-scale data pipelines
- Strong intuition for dataset quality, filtering, labeling, evaluation, and edge cases
- Ability to decompose customer goals into models, heuristics, infrastructure, and quality-assurance steps
- Ability to write clean, maintainable code and move quickly without creating brittle systems
- Passion for video, media technologies, and frontier AI applications
- Motivation to deliver end-to-end outcomes rather than only training models or writing research code
- Experience with large-scale video, audio, or multimodal data processing is a bonus
- Active open-source contribution is a bonus
- Experience as an early startup hire is a bonus
Benefits
- 401k and full health insurance
- Breakfast, lunch, dinner, and snacks provided
- Ubers home covered
- In-person work at the San Francisco headquarters
Categories
Forward DeployedML Engineering
About Sieve
Sieve builds datasets and infrastructure for training video AI models, combining large-scale video processing with novel video understanding techniques. Its tools support ML teams working on media, gaming, AR/VR, and robotics by supplying high-quality video training data and pipelines. Founded in 2016 and headquartered in Palo Alto, the company operates as a privately held AI lab focused exclusively on video data.