About the Role
As a Forward Deployed Engineer at Sieve, youβll work on highly specific dataset problems for frontier AI labs and build the custom algorithms, models, and pipelines needed to solve them.
We're looking for someone with a strong bias to action who likes working closely with customers, untangling messy requirements, and shipping fast. Youβll work closely with customers and internal teams to understand exactly what data is needed, then turn ambiguous requirements into production systems that can find, generate, filter, transform, evaluate, and package high-quality video datasets at scale.
The work often spans computer vision, audio processing, text processing, metadata analysis, model adaptation, and quality evaluation. You should be comfortable moving between research prototypes and reliable production pipelines, using models and APIs creatively, and squeezing performance through pre/post-processing, parallelism, inference optimization, fine-tuning, and evaluation loops.
Requirements
Comfortable working directly with customers or external teams to translate ambiguous needs into concrete technical systems
Strong Python developer with hands-on experience in PyTorch or similar ML frameworks
Experience building custom algorithms, model workflows, or large-scale data pipelines
Strong intuition for dataset quality, filtering, labeling, evaluation, and edge cases
Able to break customer-level goals down into the models, heuristics, infrastructure, and QA steps needed to deliver
Writes clean, maintainable code and can move quickly without creating brittle systems
Deep passion for video, media technologies, and frontier AI applications
Motivated by delivering end-to-end outcomes, not just training models or writing research code
Bonus: Experience with large-scale video, audio, or multimodal data processing
Bonus: Active contributor to open source projects
Bonus: Experience as an early hire at a startup
In-person at our SF HQ
Benefits
401k + Full Health Insurance
Breakfast, Lunch, and Dinner covered and your choice of snacks
Ubers covered home