Member of Technical Staff, Machine Learning
Machine Learning Engineer owning the full ML lifecycle for multimodal video datasets at Sieve. Fine-tune VLMs, build evaluation/QA pipelines with frontier models, design filtering systems over internet-scale data, and ship production improvements for top AI labs. Requires strong Python, PyTorch, and production ML experience.
About the job
What You'll Do
- 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 frontier models like 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 continuously improve model quality
- Develop production ML pipelines spanning preprocessing, inference, post-processing, and quality validation
- Work directly with frontier AI labs to translate ambiguous requirements into scalable ML systems
- Ship improvements quickly, measure results, and iterate based on real-world performance
Requirements
- Strong Python engineer with experience building production ML systems
- Experience training, fine-tuning, or deploying modern deep learning models
- Comfortable working with PyTorch and modern foundation models
- Excellent intuition for evaluation, dataset quality, precision/recall tradeoffs, and edge cases
- Enjoys rapidly prototyping with new AI models and APIs
- Comfortable owning projects from customer problem to internal pipelines to deployed solution
- Strong communicator who enjoys working directly with customers and cross-functional teams
- Excited by video, multimodal AI, and frontier foundation models
Nice-to-Haves
- In-person at our SF HQ (all roles require onsite in San Francisco 5 days per week)
Skills
Python, PyTorch, Deep Learning, Multimodal Models, Vision-Language Models, Fine-Tuning, Model Evaluation, Gemini, Gpt, Claude, Vlm, Ml Pipelines
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