Sieve
Member of Technical Staff, Machine Learning
today
150000 –350000 USD / yearUSALeadmachine learningdata analysispythonmodel evaluationpipeline development
Machine Learning Engineer owning the entire ML lifecycle to improve dataset quality and ship production pipelines.
About the company
- Sieve is an AI research lab building the world's highest-quality multimodal datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure, novel multimodal understanding techniques, and dozens of proprietary data sources to develop datasets 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've partnered with the world's top AI labs and did $XXM last quarter alone, as a team of just ~25 people. We also raised our Series A from Tier 1 firms such as Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
Responsibilities
- As a Machine Learning Engineer at Sieve, you'll own the entire ML lifecycle — from understanding customer problems, to designing datasets, improving models, building evaluation systems, and shipping production pipelines that deliver measurable improvements in dataset quality.
- You'll work directly with frontier AI labs to understand difficult data problems, then build end-to-end systems that solve them. One week you might fine-tune a multimodal model to improve recall on a difficult edge case. The next you might engineer a VLM-based QA pipeline, design a new evaluation framework, or run a large-scale filtering pipeline on millions of hours of multimodal data.
- We're looking for engineers who enjoy owning problems end-to-end, from understanding customer requirements through shipping production ML systems that measurably improve dataset quality.
- 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
- *all roles at Sieve require you to be onsite in San Francisco 5 days per week
Other
- Sieve is one of the most capital-efficient teams in AI — roughly 25 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.