About the job Sieve — Member of Technical Staff, Applied Research
Sieve — Member of Technical Staff, Applied Research
Type: Full-time | On-site | San Francisco, CA Compensation: $150,000–$350,000 + 0.05%–0.4% equity Hiring count: 4 Visa sponsorship: Yes — H-1B, O-1, OPT Reports to: TBD (CTO / CEO are in the loop per interview process)
About Sieve
Sieve is the only AI research lab exclusively focused on video data, combining exabyte-scale video infrastructure, novel video understanding techniques, and dozens of data sources to build datasets that push the frontier of video modeling. Video makes up ~80% of internet traffic and underpins creativity, communication, gaming, AR/VR, and robotics; Sieve targets the biggest bottleneck to progress in those domains — high-quality training data. The company has partnered with top AI labs and generated meaningful revenue last quarter with a team of just 12.
Founded: 2022 | Team size: ~12 (listed 11–50) | Total funding: Series A (amount undisclosed) Investors: Matrix Partners, Swift Ventures, Y Combinator, AI Grant Industry: AI Tools / Video data Website: sievedata.com Office: San Francisco, CA
Why Candidates Should Join
- Frontier video AI: The only research lab working exclusively on video data, partnering with top AI labs at internet scale.
- Tiny, elite team: ~12 people, revenue-generating, recently raised a Series A from Tier 1 firms.
- Real ownership: Build research building blocks and pipelines that directly shape what downstream AI models and agents can do.
Intake Call Summary
- Intake video on file; no transcript available to summarize. Re-confirm key points with David if needed before scoring.
The Role
As an applied research engineer, you'll build high-performance building blocks and large-scale pipelines to understand video with high precision at internet scale — frequently working on ambiguous research problems and finding clever techniques to solve them, across computer vision, audio processing, and text processing.
What You'll Be Doing
- Build high-performance research building blocks and large-scale video understanding pipelines
- Squeeze performance out of models + APIs via pre/post-processing, parallelism, pipelining, inference optimization, and occasional fine-tuning
- Work across computer vision, audio processing, and text processing domains
- Take problems from ambiguous research questions to production systems
Tech stack: Python, PyTorch (or similar ML frameworks)
Requirements
- 3+ years of experience in computer vision or audio processing
- Strong Python developer with hands-on experience in PyTorch or similar ML frameworks
- Excellent communication skills with customers and external teams
- Write clean, maintainable code
- Deep passion for video domain and media technologies
- Motivated by building end-to-end products
- Break down problems from customer level impact to building blocks
- In-person at SF HQ
Green Flags
- Research background, able to work independently on problems
- Experience at top tech companies/research labs
- Direct experience in computer vision/robotics
Red Flags
- Less than 3 years of relevant experience
- Lack of research/independent work experience
- No background at top-tier tech/research companies
Role Details
Salary$150,000–$350,000Equity0.05%–0.4%On-site policy In-person at SF HQ Visa sponsorshipH-1B, O-1, OPT Employment type Full-time Location San Francisco, CA
Screening Questions
From the Required Candidate Q&A card — section was truncated behind "Show More"; only the first three captured. Re-paste expanded for the full list.
- LinkedIn URL
- Where are you currently based?
- Are you willing to relocate to San Francisco? (If you already live here, click yes.)
Interview Process
Stage 1 — Initial Screen — Recruiter/early screen. Stage 2 — Technical Chat with CTO — Technical discussion with the CTO. Stage 3 — Technical Interview — Deeper technical evaluation. Stage 4 — Chat with CEO — Conversation with the CEO. Stage 5 — On Site — In-person on-site round. Stage 6 — Offer Extended Stage 7 — Candidate Hired — Candidate accepts and starts.
(Contrario pipeline also lists "Pending Approval" and "Application Review" as preceding platform/admin stages.)
Ideal Companies & Backgrounds
Updated June 26, 2026 Top tech / research labs & robotics — Meta, NVIDIA, OpenAI, Google DeepMind, Waymo, Google, Intrinsic, David AI (audio datasets for speech & conversational AI)
Ideal Candidate Profiles
For reference only — do not source these specific profiles. Marked "DO NOT CONTACT" on the role page. LinkedIn URLs were not exposed as text in the HTML (icon links only).
- Felipe Moreno
- Vahe (Vage) Taamazyan
- Gaurang Bharti
- Ben Caffee
- Geoffrey Bradway
- Jiqi Yang
- Jerry Ji
- Aun Siddiqui
- Jack Langerman
- Thanos Papadopoulos
Rejected Candidate Feedback
- None provided on the role page. Activity log shows multiple candidates rejected by the company (no reasons given).