Forward Deployed Engineer

Forward Deployed Engineer

Type: Full-time | On-site | San Francisco, CA
Compensation: $150,000 – $250,000 + competitive equity
Experience: 1 – 3 years
Hiring count: 4 (hiring multiple)
Visa sponsorship: Yes — H-1B, OPT
Tech stack: Python, PyTorch (or similar ML frameworks), large-scale data pipelines

Our client is an AI research lab focused exclusively on video data. Video makes up ~80% of internet traffic and is the dominant medium across creativity, communication, gaming, AR/VR, and robotics — but progress in video modeling has been bottlenecked by access to high-quality training data.

The company combines exabyte-scale video infrastructure, novel video understanding techniques, and dozens of diverse data sources to build datasets that push the frontier of video modeling — with precision, quality, and speed that has earned the trust of frontier AI labs, Fortune 100 companies, and fast‑growing generative AI startups. Beyond video, the team works on audio and multimodal data processing for AI training and evaluation.

Seed‑stage, founded 2022, San Francisco. Website:

You’ll own end‑to‑end dataset projects for customers — from untangling ambiguous requirements through shipping production systems that find, generate, filter, transform, evaluate, and package high‑quality datasets s is a high‑agency role working directly with customers and internal teams, combining research prototypes with reliable production pipelines. You’ll ship fast, move between technical domains within each project, and own customer outcomes directly.

• Work directly with customers to translate ambiguous dataset needs into concrete technical systems and delivery timelines
• Build custom algorithms, models, and large‑scale data pipelines spanning computer vision, audio processing, text processing, and metadata analysis
• Move between research prototypes and production systems, using models and APIs creatively to solve customer problems
• Break down customer‑level goals into the models, heuristics, infrastructure, and QA steps needed to deliver
• Optimize performance through pre/post‑processing, parallelism, inference optimization, fine‑tuning, and evaluation loops

• Experience building custom algorithms or ML workflows for production video, audio, or multimodal data
• Hands‑on work with large‑scale data pipelines at scale
• Background with PyTorch or similar ML frameworks in production
• Active contributor to open source projects
• Early hire experience at a startup

• 401(k)
• Full health insurance
• Breakfast, lunch, and dinner covered
• Choice of snacks
• Ubers covered home
• Competitive equity