Member of Technical Staff, Machine Learning
San Francisco$150k - $350kFull-timePosted 3mo ago
About the role
Machine Learning Engineer owning the full ML lifecycle to deliver production ML systems that improve dataset quality for multimodal data, collaborating with frontier AI labs, and shipping end-to-end solutions from problem understanding to deployment.
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 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
Qualifications
- 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
- In-person at our SF HQ
Skills
Required:PythonProduction MLPyTorchEvaluation & QACustomer collaborationEnd-to-end ownership
Last updated Sep 9, 2026. Interested? Apply on the company site.
About Sieve
Video datasets for frontier AI
High quality video data for AI applications.
Video GenerationHuman AvatarsWorld Models
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