The role
- COMP
- $230K - $300K
- EQUITY
- Competitive equity
- LOCATION
- San Francisco
- WORKPLACE
- Hybrid
- EXPERIENCE
- 5 - 10 years
- VISA
- None, Visa transfers
- STACK
- Python, Kubernetes, AWS, GCP, Azure
- INDUSTRY
- AI, Data
/roles — ROLE_418
Forward Deployed Engineer, Post-Sales
Well-funded AI startup whose data curation platform helps teams train stronger models with less compute
The company
Research-driven AI startup whose platform automatically curates very large training datasets so model builders can train better models faster and spend less on compute.
- STAGE
- growth-stage
- FUNDING
- $50M+ raised
- TEAM
- ~40 people
- FOUNDED
- 2023
- BACKING
- backed by Felicis
JD — the work
About the role
You would be the lead technical partner for the company's most important customers once they sign, guiding complex rollouts of its data curation platform across on-prem, hybrid, and multi-cloud setups. The product automatically selects and refines huge training datasets so models learn faster and perform better, regardless of data type. The engineering group is flat, has about 20 engineers, and works from the office four days out of five. You should be as comfortable in an executive meeting as in infrastructure configs.
What you'll do
- Take strategic accounts from first setup through a stable production launch of the platform
- Act as the main technical contact for each account, building lasting relationships and growing usage
- Architect secure, scalable setups for compute, storage, and networking across the three major clouds and on-prem Kubernetes
- Coordinate with research, engineering, and sales to convert customer needs into technical plans and carry feedback into the roadmap
- Visit customers on-site when important deployments need you there in person
What they're looking for
- 5+ years in post-sales technical work such as solutions, customer, or implementation engineering
- Practical experience running data infrastructure and distributed systems, including hybrid or on-prem compute
- Solid working knowledge of Azure, GCP, and AWS, from compute and storage to networking and IAM
- Background shipping ML or AI systems that run on Kubernetes, large backend platforms, or data pipelines
- Working fluency in SQL or Python, enough to debug problems and engage fully in technical discussions