---
title: 'Member of Technical Staff, Robotics Research at Sieve'
canonical: 'https://feeny.ai/job/member-of-technical-staff-robotics-research-sieve-san-francisco-ersyyjx4yn04'
type: 'job'
last_seen: '2026-10-29'
---

# Member of Technical Staff, Robotics Research at Sieve

- **Company:** Sieve
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-23
- **Last confirmed live:** 2026-10-29
- **Apply:** https://jobs.ashbyhq.com/sieve/35f1be09-921a-4a3c-a393-9f0a4a1a74cf

## Job description

## About Us

Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data.

We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as [Matrix Partners](https://matrix.vc/), [Swift Ventures](https://www.swift.vc/), [Y Combinator](https://www.ycombinator.com/), and [AI Grant](https://aigrant.com/).

## About the Role

As our first Robotics Researcher at Sieve, you'll lead the training and evaluation of world-action models (WAMs) and 3D perception systems to understand how data shapes robot capabilities. Your work connects how we capture, curate, and label physical-world data to measurable gains in robot learning.

You'll run your projects from question to answer: spot where a model falls short, decide what data might close the gap, set up the experiment, and test the result on tasks and environments the model hasn't seen. A lot of this work is in 3D. Raw multimodal and egocentric video becomes useful for robot learning only once it carries geometry, pose, and action, and you'll build the systems that add them. Simulation is a tool you'll use when it makes evaluation or augmentation cheaper, not the center of the job.

## What You’ll Do

- Data ablations for robot learning: Compare data sources such as egocentric human video, teleop, and robot rollouts, and vary quality, mix, embodiment, and supervision to learn which choices improve policies and world models.
- World-action models: Train and post-train action-conditioned world models and policies, pushing them toward skills like following commanded actions, manipulating objects, and responding to camera motion.
- 3D labeling from video: Build pipelines for depth, camera trajectories, hand/body/object pose, point tracks, and scene reconstruction, so raw footage becomes action-labeled data grounded in 3D.
- Evaluation design: Lock the test tasks, environments, and embodiments before comparing models, and report clear, checkable conclusions about which data made the difference.
- Simulation as a tool: Use simulators or learned world models for evaluation, augmentation, and sim-to-real checks.
- From findings to datasets: Turn results into the curation filters, annotation specs, and capture protocols behind our pipelines and the data we deliver to partner labs.
- Partner collaboration: Work with Sieve engineers and researchers at partner labs to pick the right problems and share what you find.
- Publish what you learn: Share findings through papers, technical reports, blog posts, and open datasets or benchmarks where it makes sense, and help shape how the field thinks about data for robot learning.

## Requirements

- 4+ years of research or engineering experience in robot learning, embodied AI, or 3D computer vision. A PhD is welcome but not required; strong publications, open-source projects, or systems you can walk us through count just as much.
- Fluent Python plus PyTorch or JAX: you can write, debug, and change training code yourself
- Hands-on 3D vision work: multi-view geometry, depth, pose, tracking, or reconstruction
- Have trained a policy or world model (VLA, diffusion policy, imitation learning, action-conditioned video model, etc.) and tested it on data it never saw in training
- Rigorous about experiments: you set baselines, run ablations, and can tell whether a gain came from the data or from architecture, compute, or a leaky eval
- Comfortable with large, messy datasets and diagnosing data quality issues
- Strong communication skills and the ability to explain findings, tradeoffs, and uncertainty clearly
- In-person at our SF HQ

Bonus

- Robot learning from egocentric or human video, including retargeting human motion onto robot embodiments
- Have put a trained policy on real robot hardware and measured it in closed loop
- Simulators like MuJoCo or Isaac Sim, used for evaluation or synthetic data
- Large-scale distributed training
- Experience as an early hire at a startup

## Benefits

- 401(k) and full health insurance
- Breakfast, lunch, and dinner covered, plus your choice of snacks
- Ubers covered home

*All roles at Sieve require you to be onsite in San Francisco 5 days per week

## About Sieve

## Company Overview
- **One-liner**: Sieve is an AI research lab and platform focused on building high-quality multimodal data (video, audio, image, and interaction data) and infrastructure for training frontier AI models.
- **Entity Type**: Private (Seed-stage)
- **Headquarters**: San Francisco, California, USA
- **Founded**: 2022
- **Founders**: Mokshith Voodarla (CEO) and Abhinav "Abhi" Ayalur (CTO)

## Core Business
- Primary industry: Artificial Intelligence / Data Infrastructure / Computer Vision
- Target customers: Frontier AI labs, Fortune 100 companies, and fast-growing AI startups working on generative media, robotics, computer use, world models, and agentic systems (primarily B2B Enterprise).
- Mission statement: To build the data and environments that frontier AI labs use to train the next generation of multimodal systems.

## Products & Services
- **Sieve Platform (sievedata.com)**: A development platform to discover, design, and run AI features at scale. It offers pre-built, production-ready apps and models (e.g., `sieve/speech_transcriber` for transcription, `sieve/seamless_text2text` for translation) that can be used via a single API call. The platform provides no-code interfaces, reliable infrastructure, and primitives like queues and async jobs. It is focused on making it easy to combine multiple AI models into complex applications (e.g., video dubbing, translation). [docs.sievedata.com]
- **Sieve Data Lab (sieve.ai)**: A multimodal data lab offering high-quality video, audio, image, and interaction datasets for frontier AI. Services include custom data collection, indexing billions of data points, filtering for semantics and rights, adding dense annotations (transcripts, object labels, UI events), and secure delivery with end-to-end encryption and SOC 2 Type 2 compliance. [sieve.ai]
- **Video AI API (Beta)**: A recently launched API that allows developers to use Sieve's pre-built AI apps and models for video processing in a single call. [sievedata.com]

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Raised approximately **$4M** in a seed round in 2022. [ycombinator.com, sievedata.com]
- **Notable Investors/Partners**: Y Combinator (Winter 2022 batch, with Diana Hu as Primary Partner), AI Grant (Nat Friedman & Daniel Gross). [ycombinator.com, sieve.ai/about]
- **Growth Signals**: The company has evolved from a general AI platform to a specialized research lab focusing on the critical bottleneck of training data for multimodal AI. It has attracted the trust of "frontier AI labs, Fortune 100 companies, and fast-growing AI startups." The team size is listed as 18 on Y Combinator (2022) and they are actively hiring for multiple engineering roles through the end of 2026, indicating scaling. [ycombinator.com, jobs.ashbyhq.com]

## Competitive Advantages
- **Focus on Data as a Moat**: Sieve is explicitly structured as a "research lab exclusively focused on video data," identifying that high-quality training data is the primary bottleneck for the next generation of AI (video, audio, robotics, agentic systems). [ycombinator.com]
- **End-to-End Data Service**: They provide a complete pipeline from custom collection and indexing to dense annotation and secure delivery, unlike many competitors who only offer one piece.
- **Deep Research Partnerships**: The team works directly with leading AI researchers to understand model bottlenecks and design high-signal datasets, creating a tight feedback loop between research needs and data production.
- **Strong Founder-Market Fit**: Founders Mokshith Voodarla (Scale AI, NVIDIA) and Abhinav Ayalur (NVIDIA, Niantic) have direct experience in building computer-vision systems and experiencing the data difficulties first-hand.

## Strategic Focus
- **Deliver Training Data for Frontier AI**: Sieve’s current priority is to scale its exabyte-scale infrastructure and novel multimodal understanding techniques to create datasets for video, audio, images, software, robotics, and interactive worlds. [sieve.ai/about]
- **Enable Complex AI Use Cases**: The platform side focuses on making it easy for developers to combine multiple models to build production-ready AI features (like translation, dubbing) quickly.
- **Data Security and Compliance**: Emphasizing secure, compliant data delivery with SOC 2 controls is a key priority for servicing Fortune 100 companies.

## Why Work Here
- **Culture**: The company describes itself as "tight-knit, fast-moving, and deeply customer-oriented." It is a "research lab to the core," combining frontier research with infrastructure and customer partnership. [sieve.ai/about]
- **Team**: The team brings together experience from NVIDIA, Scale AI, Zoox, and Niantic, providing exposure to top-tier expertise in AI and infrastructure. [sieve.ai/about]
- **Impact**: Employees will be working on the critical layer of data for the next generation of AI models (generative media, robotics, agents), offering a rare position at the forefront of AI research. [ycombinator.com]
- **Remote/Hybrid/Office**: The company is based in San Francisco, CA.
- **Open Roles**: As of mid-2026, they are actively hiring for **Applied Research Engineer**, **Distributed Systems Engineer**, **Product Engineer**, and **Software Engineer**, with salary ranges listed as $150K - $300K depending on role and experience. [jobs.ashbyhq.com, ycombinator.com]

## Sources
1. [docs.sievedata.com](https://docs.sievedata.com/guide/intro)
2. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/sieve)
3. [sieve.ai](https://www.sieve.ai/)
4. [sieve.ai/about](https://www.sieve.ai/about)
5. [ycombinator.com](https://www.ycombinator.com/companies/sieve)

## Other roles at Sieve

- [Recruiting Ops Associate](https://feeny.ai/job/recruiting-ops-associate-sieve-san-francisco-e3h4mvdm6nac) — San Francisco, CA
- [Data Collection Partnerships Lead](https://feeny.ai/job/data-collection-partnerships-lead-sieve-san-francisco-6m4vwyfgbq4h) — San Francisco, CA
- [Country Lead](https://feeny.ai/job/country-lead-sieve-remote-mtfvhf9mg5jw)
- [Member of Technical Staff, Computer Vision (Perception)](https://feeny.ai/job/member-of-technical-staff-computer-vision-perception-sieve-san-francisco-nbgf8j2bmke1) — San Francisco, CA
- [Member of Technical Staff, Robotics](https://feeny.ai/job/member-of-technical-staff-robotics-sieve-san-francisco-rndazrv6c3xh) — San Francisco, CA
- [Operations Associate](https://feeny.ai/job/operations-associate-sieve-remote-p8f14g1ssyvt)
- [Industrial Partnerships Lead, Physical AI](https://feeny.ai/job/industrial-partnerships-lead-physical-ai-sieve-san-francisco-399z6fq1nh8x) — San Francisco, CA
- [Business Development](https://feeny.ai/job/business-development-sieve-san-francisco-crb7611fxcpw) — San Francisco, CA
- [Growth Associate](https://feeny.ai/job/growth-associate-sieve-india-8qjbpndm06dg) — India
- [Member of Technical Staff, Forward Deployed](https://feeny.ai/job/member-of-technical-staff-forward-deployed-sieve-san-francisco-kcexg4yd4whn) — San Francisco, CA