Sieve · San Francisco ·
Build and fine-tune multimodal AI models and pipelines to improve dataset quality for video understanding, working directly with frontier AI labs in San Francisco.
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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.
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
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
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