Sieve
Member of Technical Staff, Machine Learning
San Francisco · Staff+
Sponsorship not specifiedDetected 102 days ago
PythonSwiftMachine LearningDeep LearningPyTorchNLPMLOpsAR/VRCommunication
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odds of building a lasting career here
34Risky
Cap-exempt (no lottery)0
Sponsors this role70
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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Files H-1B transfers
5 transfer filings in the last year, covering 5 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- One week you might fine-tune a multimodal model to improve recall on a difficult edge case.
Responsibilities
- Own model quality for customer-facing video understanding problems
- 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
- Strong Python engineer with experience building production ML systems
- You'll work directly with frontier AI labs to understand difficult data problems, then build end-to-end systems that solve them.
Benefits
- Fine-tune vision-language and multimodal foundation models for specialized tasks
- Experience training, fine-tuning, or deploying modern deep learning models
Company info
- 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 https://matrix.vc/, Swift Ventures https://www.swift.vc/, Y Combinator https://www.ycombinator.com/, and AI Grant https://aigrant.com/.
- 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.
- 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 https://matrix.vc/, Swift Ventures https://www.swift.vc/, Y Combinator https://www.ycombinator.com/, and AI Grant https://aigrant.com/.
- 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.
- Sieve exists to solve the biggest bottleneck in the growth of these applications:
- high-quality training data.
This listing is sourced directly from Sieve's careers page and normalized into a canonical job model.