ML Engineer, Multimodal Data & Production (SF Onsite)

Sieve; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Sieve is seeking a Machine Learning Engineer in San Francisco to own the entire ML lifecycle for multimodal data—from problem understanding to dataset design, model improvement, evaluation systems, and production pipelines delivering measurable gains in data quality. You will collaborate with frontier AI labs, fine-tune models, build filtering and QA systems, and ship improvements quickly.

On-site in SF five days a week, you will drive end-to-end solutions that directly impact model performance

Qualifications

• Strong Python engineer with experience building production ML systems.
• Experience training, fine-tuning, or deploying modern deep learning models.
• Comfortable with PyTorch and foundation models.
• Excellent intuition for evaluation, data quality, precision/recall tradeoffs.
• Able to prototype rapidly with new AI models and APIs.
• Ownership from customer problem to deployed solution.
• Excellent communication with customers and cross-functional teams.
• Onsite in San Francisco five days a week.

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.

Skills

Python

Production ML

PyTorch

Model deployment

Evaluation & QA

Data pipelines

Communication

Video / multimodal

Edge-case handling

Customer collaboration

Job description

Sieve is seeking a Machine Learning Engineer in San Francisco to own the entire ML lifecycle for multimodal data—from problem understanding to dataset design, model improvement, evaluation systems, and production pipelines delivering measurable gains in data quality. You will collaborate with frontier AI labs, fine-tune models, build filtering and QA systems, and ship improvements quickly.

On-site in SF five days a week, you will drive end-to-end solutions that directly impact model performance