Latest AI JobsBrowse Jobs
U

ML Platform Engineer

universalagiSan FranciscoTechnical Staff
Apply on company site
Salary
Not listed
Job type
Full-time
Experience
Mid
Posted
24 Sept 2026
Status
Open · checked 7d ago

About this role

This role suits an experienced infrastructure engineer who thrives in an intensive, fully onsite startup environment and wants to build foundational systems for AI-driven physics simulation. UniversalAGI is seeking an ML Platform Engineer to work in San Francisco, designing and managing the execution platform for internal research and external customer deployments.

Role Overview

UniversalAGI is hiring an ML Platform Engineer to build and own the execution platform powering research and customer deployments. The position is based onsite in San Francisco, requiring five days a week in the office. Working directly with the CEO and founding team, this role covers data generation, simulation orchestration, training and fine-tuning infrastructure, benchmarking pipelines, and production deployments.

Key Responsibilities

Build and operate scalable infrastructure for data generation and simulation workflows, including job orchestration, scheduling, queues, retries, and observability.
Construct reproducible pipelines for training, fine-tuning, and benchmarking that handle artifact and version management, experiment tracking, and dataset lineage.
Manage cost and performance trade-offs across compute, storage, networking, and runtime efficiency.
Lead deployments of the technology stack into customer cloud and on-prem environments across AWS, GCP, Azure, and hybrid setups, covering secure networking, permissions, and data movement.
Create robust deployment patterns incorporating environment provisioning, CI/CD, rollbacks, monitoring, and incident response.
Partner directly with clients to guarantee reliability and repeatability under real-world security, compliance, and infrastructure constraints.

Requirements

Strong software engineering abilities demonstrating clean code, debugging, reliability, and reproducibility.
Practical experience building and operating infrastructure for machine learning and compute-heavy workflows such as pipelines, job orchestration, GPU compute, storage, CI/CD, and monitoring.
High standards for personal performance with a focus on measurable improvement on delivery metrics.
Resourcefulness in balancing quick fixes against robust, long-term solutions with justified impact.
Total ownership mindset, taking end-to-end accountability for measurable outcomes.

Nice to Have

Experience with workflow orchestration tools like Ray, Kubernetes, or Slurm.
Background in GPU infrastructure and distributed training systems.
Experience constructing evaluation and benchmarking frameworks that guarantee strong reproducibility.
Deploying solutions into regulated or security-sensitive environments such as government, defense, or enterprise sectors.
Prior exposure to simulation and High-Performance Computing pipelines including CFD, meshing, and batch workloads.
Background in Forward Deployed Engineering or similar rapid delivery execution roles.

Skills and Tools

AWS
GCP
Azure
Ray
Kubernetes
Slurm

Salary and Benefits

Competitive salary combined with equity upside.
Company-paid health, dental, and vision benefits.
401(k) plan offering.
Flexible vacation policy.
AI tools stipend, monthly commute stipend, and monthly wellness or fitness stipend.
Daily office lunch and dinner covered by the company.
Immigration support.
Team building and fun activities.

About the Company

UniversalAGI builds foundation models that learn physics directly from data to replace decades-old numerical simulation methods used in aerospace, shipping, reservoirs, and medical devices. Backed by prominent investors and technology leaders, the team of twenty-five researchers and engineers operates out of San Francisco and deploys early solutions for major industrial and defense organizations.

How to Apply

Interested candidates should apply on the official company careers page.

Skills and tech stack

AWSGCPAzureRayKubernetesSlurmFine-tuning

Questions candidates ask

Where is the ML Platform Engineer position located?

The role is based onsite in San Francisco, requiring five days a week in the office.

What kind of compensation is offered for this role?

The position offers a competitive salary along with equity upside, company-paid health, dental, and vision benefits, a 401(k) plan, stipends for AI tools, commuting, and wellness, and daily catered lunches and dinners.

Is visa or immigration support available?

Yes, immigration support is provided.

Who will I work with most closely in this role?

You will work closely with the CEO and the founding team to turn research into scalable, reliable systems.

What are the core responsibilities regarding customer deployments?

Responsibilities include leading stack deployments into customer cloud and on-prem environments across AWS, GCP, Azure, and hybrid setups, building robust deployment patterns, and partnering with clients to ensure reliability under real-world constraints.

Ready to apply?

The application is on the official universalagi careers page.

Apply on company site