Sieve
Engineering
MemberofTechnicalStaff,DeployedResearch
“Member of Technical Staff, Deployed Research at Sieve. Skills: Deployed Research Engineering, dataset problems, custom algorithms, models, pipelines, production systems, high-quality video datasets, computer vision, audio processing, text processing, metadata analysis, model adaptation, quality evaluation, Python, PyTorch. work on highly specific dataset problems for frontier AI labs. build the custom algorithms, models, and pipelines needed to solve them”
What You'll Achieve.
deliver end-to-end outcomes; shipping fast
Industry & Context.
work on highly specific dataset problems; untangling messy requirements; turn ambiguous requirements into production systems; Able to break customer-level goals down into the models, heuristics, infrastructure, and QA steps needed to deliver
In-person at our SF HQ
What They're Looking For.
Must Have
Python developer with hands-on experience in PyTorch or similar ML frameworks, Experience building custom algorithms, model workflows, or large-scale data pipelines, 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
Nice to Have
Experience with large-scale video, audio, or multimodal data processing, Active contributor to open source projects, Experience as an early hire at a startup
What You'll Do.
work on highly specific dataset problems for frontier AI labs
build the custom algorithms
and pipelines needed to solve them
understand exactly what data is needed
turn ambiguous requirements into production systems that can find
and package high-quality video datasets at scale
move between research prototypes and reliable production pipelines
use models and APIs creatively
squeeze performance through pre/post-processing
inference optimization
How You'll Work.
Team & Collaboration
work closely with customers; work closely with customers and internal teams; Comfortable working directly with customers or external teams
Communication Scope
translate ambiguous needs into concrete technical systems
Process & Methodology
Able to break customer-level goals down into the models, heuristics, infrastructure, and QA steps needed to deliver
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