Full-time

Infrastructure Engineer

Elicit

Posted

Jul 22, 2026

Location

Remote (US)

Type

Full-time

Compensation

$250000 - $280000

Mission

What you will drive

  • Own cloud infrastructure across AWS and GCP, including Kubernetes clusters, networking, databases, and CI/CD pipeline.
  • Scale single-tenant deployments from a handful to many, making private cloud deployment a repeatable, low-overhead operation.
  • Build observability and incident response practice, and drive compliance and security operations (SOC 2, NIST AI framework, EU Cyber Resilience).
  • Manage infrastructure cost and capacity, and improve developer experience.

Impact

The difference you'll make

This role ensures the reliability, security, and cost-efficiency of Elicit's AI research platform, directly enabling high-stakes evidence synthesis for scientists, pharma companies, and decision-makers, thereby improving the quality of reasoning and decisions that impact millions of dollars and public health.

Profile

What makes you a great fit

  • 5+ years of hands-on infrastructure/SRE/platform engineering experience.
  • Solid Terraform experience and strong Kubernetes expertise (production clusters).
  • AWS experience (primary), with GCP familiarity a plus.
  • GitOps and CI/CD fluency (Argo CD, GitHub Actions), SRE mindset, security and compliance awareness (SOC 2), and ability to write software.

Benefits

What's in it for you

Flexible work environment (Oakland office or remote with time zone overlap GMT to GMT-8), fully covered health/dental/vision/life insurance, flexible vacation (min 20 days/year), $200 monthly wellbeing stipend, 401K with 6% match, new Mac + $1,000 home office setup (then $500/year), $1,000 quarterly AI experimentation budget, team administrative assistant. Compensation: Senior (L4) $185-260k + equity, Expert (L5) $250-280k + equity, Principal (L6) >$260k + significant equity.

About

Inside Elicit

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Elicit is a public benefit company developing an AI research assistant to help researchers make better decisions. Their product aims to be a scalable ML system prioritising systematicity and transparency, with supervision of process, not outcomes.