I keep seeing ML infrastructure jobs labeled “entry level” that actually require 5+ years of experience.

I checked the official employer pages and found three active roles with requirements that are genuinely closer to early career.

1) AppLovin — ML Infrastructure Engineer

• Location: Palo Alto, California
• Experience: 0–2 years
• Education: BS and/or MS in Computer Science
• Relevant skills: data structures, algorithms, C++, Python, or Go
• Focus: distributed systems and infrastructure supporting model training and serving
• California base-pay range: $124,000–$250,000
• The posting says its application window is expected to remain open for 30 days from the posting date

Official posting: https://job-boards.greenhouse.io/applovin/jobs/4655740006

2) Astera Labs — Machine Learning Infrastructure Engineer

• Location: San Jose, California
• Experience: 1–5 years
• Relevant skills: Python, AWS or GCP, inference deployment, observability, and production service ownership
• Focus: infrastructure for LLM applications, agents, model routing, telemetry, evaluations, and reliability
• Base-pay range: $140,000–$165,000

Official posting: https://job-boards.greenhouse.io/asteralabs/jobs/4706340005

3) Goldman Sachs — Machine Learning Platform Associate

• Location shown: Jersey City, New Jersey
• Minimum experience: 2 years in backend, platform, or infrastructure software engineering
• Also requests: 2 years with Python or a similar backend language and 1 year supporting production ML systems
• Relevant skills: APIs, containers, Unix, cloud infrastructure, databases, testing, and debugging
• Focus: MLOps and production systems for deploying and monitoring ML and LLM workloads

Official posting: https://higher.gs.com/roles/171221

The common thread is that these are closer to backend and platform engineering than ML research. Experience with distributed systems, cloud infrastructure, deployment, reliability, or backend services may be more relevant than training models from scratch.

Verified against the employers’ official career pages on July 22, 2026. “Verified” only means the pages were active and the details above appeared when checked; employers can change or close a position without notice.

What should the next verified roundup focus on: full-time new-grad SWE roles, internships, or jobs that state their sponsorship policy?