3 months ago
Base Salary
$150k - $350k/yr
Responsibilities
- Own model quality for customer-facing video understanding problems.
- Fine-tune vision-language and multimodal foundation models for specialized tasks.
- Build automated evaluation and QA pipelines using models such as Gemini, GPT, Claude, and open-source VLMs.
- Design high-precision filtering, ranking, retrieval, and labeling systems over internet-scale video datasets.
- Create datasets, benchmarks, and evaluation frameworks that improve model quality.
- Develop production ML pipelines covering preprocessing, inference, post-processing, and quality validation.
- Translate ambiguous requirements from frontier AI labs into scalable ML systems.
- Ship improvements quickly, measure results, and iterate based on real-world performance.
Requirements
- Strong Python engineering skills and experience building production machine learning systems.
- Experience training, fine-tuning, or deploying modern deep learning models.
- Experience with PyTorch and modern foundation models.
- Strong understanding of evaluation, dataset quality, precision/recall tradeoffs, and edge cases.
- Ability to rapidly prototype with new AI models and APIs.
- Ability to own projects from customer problem through internal pipelines and deployed solutions.
- Strong communication skills and interest in working directly with customers and cross-functional teams.
- Interest in video, multimodal AI, and frontier foundation models.
Benefits
- The role is onsite at Sieve's San Francisco headquarters five days per week.
Categories
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.