Artificial Intelligence Engineer

They’re looking for a Machine Learning Engineer to own ML problems end to end, from understanding customer needs and designing datasets to improving models, building evaluation systems, and shipping production pipelines.

You’ll work directly with frontier AI teams on challenging problems involving model quality, dataset quality, evaluation, filtering, ranking, retrieval, and multimodal understanding.

This role is a strong fit for an engineer who enjoys building production ML systems, experimenting with new models, and taking ambiguous problems from initial requirements through deployment.

What You’ll Work On

• Build and improve production machine learning systems in Python
• Train, fine-tune, and deploy modern deep learning and foundation models
• Develop evaluation and QA pipelines using models such as GPT, Claude, Gemini, and open-source models
• Design systems for filtering, ranking, retrieval, labeling, and dataset curation
• Build benchmarks and evaluation frameworks for model and dataset quality
• Analyze precision, recall, edge cases, and failure modes to improve system performance
• Develop scalable ML pipelines spanning preprocessing, inference, post-processing, and quality validation
• Work directly with customers and cross-functional teams to translate complex requirements into production systems
• Rapidly prototype with new models, tools, and APIs

• 2+ years of relevant experience
• Experience building and shipping production machine learning systems
• Experience training, fine-tuning, or deploying modern deep learning models
• Hands-on experience with PyTorch
• Experience working with modern foundation models
• Strong understanding of model evaluation, dataset quality, precision and recall tradeoffs, and edge cases
• Ability to own technical projects from problem definition through deployment
• Strong communication skills and comfort working with customers and cross-functional teams
• Able to work onsite at Sieve’s San Francisco office 5 days per week, * Experience with computer vision, video understanding, or multimodal AI
• Experience with vision-language models or other multimodal foundation models
• Experience building large-scale data or ML pipelines
• Experience with data filtering, curation, ranking, or retrieval systems
• Experience rapidly prototyping with new AI models and APIs
• Background at an AI company, ML infrastructure company, data platform, or high-scale engineering organization