ML Engineer: Multimodal Data & Production Systems

Sieve, Inc.; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Sieve, Inc. is seeking a Machine Learning Engineer in San Francisco to own the entire ML lifecycle—from understanding customer problems to shipping production pipelines that improve dataset quality.

You'll work directly with frontier AI labs to tackle challenging multimodal data problems, fine-tune models, build evaluation systems, and deliver measurable improvements in dataset quality across video, audio, images, and text. This is an onsite role requiring full-time commitment.

Qualifications

• Proficient in Python with experience building production ML systems.
• Experience training, fine-tuning, or deploying modern deep learning models.
• Comfortable with PyTorch and modern foundation models.
• Strong evaluation instincts for dataset quality, precision/recall, and edge cases.
• Able to prototype rapidly with new AI models and APIs.
• Owns projects end-to-end from problem to deployment.

Responsibilities

• Own model quality for customer-facing video understanding challenges.
• Fine-tune vision-language and multimodal models for specialized tasks.
• Build automated evaluation and QA pipelines using frontier models and open-source VLMs.
• Design high-precision filtering, ranking, retrieval, and labeling over large video datasets.
• Create datasets, benchmarks, and evaluation frameworks to improve model quality.
• Develop production ML pipelines covering preprocessing, inference, post-processing, and validation.
• Collaborate with frontier AI labs to translate requirements into scalable ML systems.
• Ship improvements quickly and measure results in real-world performance.

Skills

Python

PyTorch

Production ML systems

Model fine-tuning

End-to-end ownership

Tools

PyTorch

Job description

Sieve, Inc. is seeking a Machine Learning Engineer in San Francisco to own the entire ML lifecycle—from understanding customer problems to shipping production pipelines that improve dataset quality.

You'll work directly with frontier AI labs to tackle challenging multimodal data problems, fine-tune models, build evaluation systems, and deliver measurable improvements in dataset quality across video, audio, images, and text. This is an onsite role requiring full-time commitment.