# Member of Technical Staff - ML Ops at Vinci

- Company: Vinci
- What the company does: Run full-resolution simulations in minutes. Vinci’s foundation model for physics unites AI acceleration with verified solvers for as-built accuracy. Backed by Khosla.
- Company website: https://www.getvinci.ai/
- Type: Startups (AI role)
- Level: Senior
- Location: Palo Alto HQ
- Work setup: Remote
- Pay: $185K to $245K base salary per year (USD)
- Posted: 2026-10-30
- Apply by: 2026-12-14
- Apply: https://jobs.ashbyhq.com/vinci/fc5f0f2e-d48e-46da-aa99-cb18653ebb7f?utm_source=1752vc&utm_medium=careers
- Page: https://www.1752.vc/careers/jobs/vinci-member-of-technical-staff-ml-ops/

## About the role

Our foundation model learns physics from simulations, and we generate the simulation data ourselves. The simulations encapsulate real-world, validated physical results. Our aim is to create a data corpus covering multiple types of physics over an exhaustive set of physical objects. Found on 1752vc Careers, the job board for startup and VC roles.

## What they're looking for

- 5+ years building production data or ML pipelines, including at least 2 years where you owned a pipeline that ran on GPUs at meaningful scale
- Experience running large batch or simulation workloads: thousands of long-running jobs, partial failures, retries, preemption, and making sure work is neither lost nor duplicated
- Strong Python and software engineering fundamentals: clean abstractions, typed code, and a test suite you trust
- Comfort with relationalrelationa and NoSQLl databases at scale: schema design, migrations on live data, bulk writes, and query performance once tables reach hundreds of millions of rows
- Experience with workflow orchestration or distributed execution frameworks, such as Temporal, Ray, Airflow, Argo, Kubernetes Jobs or Slurm
- Working knowledge of cloud object storage and of the file formats that feed training at high throughput

Tags: Engineering

Source: 1752vc Careers, https://www.1752.vc/careers/jobs/vinci-member-of-technical-staff-ml-ops/