Member of Technical Staff, Machine Learning

About the role

Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data. We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as Matrix Partners, Swift Ventures, Y Combinator, and AI Grant. Sieve is one of the most capital-efficient teams in AI — roughly 30 people serving the world's leading AI labs across every major data modality. You'll join early, own problems end-to-end, and watch your work ship directly into the models defining the frontier.

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

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
• In-person at our SF HQ

Pay

Compensation Range: $150K - $350K

Schedule

In-person at our SF HQ, 5 days per week