ML Platform Engineer: Build Scalable AI Pipelines

UniversalAGI, Inc.; San Francisco (CA); Hybride; USD 180 000 - 240 000; Plein temps

Avantages offerts par ce poste

Competitive equity upside

In‑person, 5 days in office

Immigration support

Résumé du poste

UniversalAGI, Inc. in San Francisco is seeking an ML Platform Engineer to build and own the execution platform powering research and customer deployments.

You will architect data generation, simulation orchestration, training pipelines, and benchmarking, while ensuring reliability and scalability in both internal and customer environments. You will work closely with the CEO and team to turn research into reusable, production‑grade systems, shaping how fast we iterate and deliver results in

Qualifications

• Strong software engineering skills (clean code, debugging, reliability, reproducibility).
• Hands‑on experience building/operating infrastructure for ML/compute‑heavy workflows: pipelines, job orchestration, GPU compute, storage, CI/CD, monitoring.
• Olympic athlete mindset: high standards and obsession with measurable improvement.
• Resourcefulness: quick fixes vs robust solutions with impact justification.
• Ownership: end‑to‑end ownership and accountability for outcomes.

Responsabilités

• Build the internal foundation platform: data generation and simulation workflows, training/fine‑tuning and benchmarking pipelines, and cost/performance optimization.
• Deploy to customer environments (AWS/GCP/Azure + on‑prem/hybrid), including secure networking, permissions, and data movement.
• Lead deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response.

Connaissances

Software engineering

Pipelines

Job orchestration

GPU compute

CI/CD

Monitoring

Ownership

Outils

Ray

Kubernetes

Slurm

CI/CD tooling

Description du poste

UniversalAGI, Inc. in San Francisco is seeking an ML Platform Engineer to build and own the execution platform powering research and customer deployments.

You will architect data generation, simulation orchestration, training pipelines, and benchmarking, while ensuring reliability and scalability in both internal and customer environments. You will work closely with the CEO and team to turn research into reusable, production‑grade systems, shaping how fast we iterate and deliver results in