[Hiring] Infrastructure Engineer @Elicit

Role Description

Elicit is an AI research platform used by scientists, pharma companies, and decision-makers for high-stakes evidence synthesis. A single session could trigger hundreds of thousands of language model invocations across multiple providers, which means our infrastructure decisions directly impact cost, reliability, and the quality of research outcomes for users making decisions worth millions of dollars.

This is the first dedicated infrastructure hire and you'll define how this function works at Elicit. This role will own and evolve the infrastructure platform that underpins Elicit's product, ensuring it is reliable, secure, cost-efficient, and ready for the demands of a growing enterprise customer base.

What you'll own

• Own our cloud infrastructure across AWS and GCP — Kubernetes clusters, networking, databases (Aurora PostgreSQL, Redis, MongoDB Atlas), Cloudflare, and our CI/CD pipeline.
• Scale single-tenant deployments from a handful to many — each with distinct data retention, geographic, monitoring, and compliance requirements.
• Build our observability and incident response practice — proactive monitoring, alerting, SLA tracking, and structured post-mortems.
• Drive compliance and security operations — ensure we follow through on the policies we've written (SOC 2, NIST AI framework, EU Cyber Resilience).
• Manage infrastructure cost and capacity — make smart decisions about where we run workloads, optimize spend, and plan capacity as usage grows.
• Improve developer experience — CI/CD pipeline performance, preview environments, local development tooling, and deployment confidence.
• Contribute to backend systems where infrastructure and application intersect.

What success will look like (6-12 months)

• A private cloud deployment is a ~1-day turnkey operation.
• Playbooks and templated Terraform make standing up Elicit in a customer's cloud routine.
• Our observability signal:noise ratio improves 10-fold.
• Health monitors cover every endpoint and job.
• Disaster recovery is practiced.
• Our SLAs are backed by engineering rigor.
• Inference is faster and cheaper.

Qualifications

• 5+ years of hands-on infrastructure/SRE/platform engineering experience.
• An AI-native way of working.
• Solid Terraform experience.
• Strong Kubernetes expertise.
• AWS experience (primary), with GCP familiarity a plus.
• GitOps and CI/CD fluency.
• SRE mindset.
• Security and compliance awareness.
• Ability to write software.

Am I a good fit?

Strong applicants will find it easy to answer these questions:

• Can you describe a time you designed and executed a multi-tenant or single-tenant deployment architecture for enterprise customers?
• How have you approached disaster recovery planning and testing at a previous company?
• Walk me through how you'd evaluate whether to build vs. buy for a new infrastructure component at a ~30-person startup.
• Have you owned compliance follow-through for a framework like SOC 2?

Who will I work with?

• James (Head of Engineering): set up the original infrastructure and will be your closest partner.
• Panda: the engineer who has been covering infrastructure part-time.
• Product: PMs covering the core product, ML, and evals.
• The whole engineering team: ~30 people company-wide.
• Andreas and Jungwon (cofounders): you'll meet both during the interview process.

On-call & incident expectations

We don't have a formal on-call rotation yet. Incidents today are handled by the engineers closest to the affected system.

Why join us?

• The work matters.
• Infrastructure work is close to the business.
• Built for the long term.
• High agency, low bureaucracy.
• Serious investment in your growth.

Location and travel

We have a great office in Oakland, CA, and we'd love to see you there if you're local. That said, we're just as happy for you to work remotely.

Benefits

• Flexible work environment.
• Fully covered health, dental, vision, and life insurance.
• Flexible vacation policy.
• $200 monthly wellbeing stipend.
• 401K with a 6% employer match.
• A new Mac + $1,000 budget to set up your workstation.
• $1,000 quarterly AI Experimentation & Learning budget.
• A team administrative assistant.

Compensation

For this role, we target starting ranges of:

• Senior (L4): $185-260k + equity
• Expert (L5): $250-280k + equity
• Principal (L6): >$260 + significant equity