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 the data and environments frontier AI labs use to train the next generation of multimodal systems. AI is moving beyond chatbots into video, audio, images, software, robotics, and interactive worlds. The next generation of models will need to understand how the world looks, sounds, moves, responds, and changes over time. Progress is bottlenecked by one thing: high-quality data. Sieve brings together exabyte-scale infrastructure, novel multimodal understanding techniques, large-scale sourcing, and deep research partnerships to create datasets and environments with unmatched precision, quality, and speed. This has earned the trust of frontier AI labs, Fortune 100 companies, and fast-growing AI startups working on generative media, robotics, computer use, world models, and agentic systems.