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Elicit

AI-powered literature review and data extraction

Infrastructure Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of hands-on infrastructure, site reliability engineering, or platform engineering experience.
  • Use AI coding agents as a regular practitioner to write infrastructure as code and investigate incidents; public writing, speaking, or projects demonstrating this are a bonus.
  • Solid Terraform experience; Terraform is the primary infrastructure-as-code layer and the most important technical requirement.
  • Production Kubernetes cluster operations expertise, including EKS, networking, Karpenter autoscaling, and debugging cluster-level issues.
  • AWS experience; familiarity with GCP is a plus.
  • GitOps and continuous integration/continuous delivery fluency with Argo CD, GitHub Actions, or equivalent, including deployment automation, rollback strategies, and change management.
  • Experience building or significantly improving observability stacks such as Datadog, incident response processes, and on-call practices.
  • Security and compliance awareness, including experience with SOC 2 or similar frameworks, security information and event management tooling, and translating compliance requirements into engineering practice.
  • Ability to write software and contribute to backend codebases where infrastructure meets application logic.

Responsibilities

  • Own and evolve cloud infrastructure across AWS and GCP, including Kubernetes clusters, networking, Aurora PostgreSQL, Redis, MongoDB Atlas, Cloudflare, and the continuous integration and continuous delivery pipeline.
  • Scale single-tenant deployments to support distinct data retention, geographic, monitoring, and compliance requirements, and make private cloud deployments repeatable and low-overhead.
  • Build observability and incident response practices, including proactive monitoring, alerting, service-level agreement tracking, and structured post-mortems.
  • Drive compliance and security operations by following established policies, owning disaster recovery exercises and database restoration drills, and monitoring security events.
  • Manage infrastructure cost and capacity, choose where workloads run across AWS, CoreWeave, and Parasail, optimize spend, and plan capacity as usage grows.
  • Improve developer experience through continuous integration and continuous delivery performance, preview environments, local development tooling, and deployment confidence.
  • Contribute to backend systems at the infrastructure/application boundary, including circuit breakers, inference routing, and data connector infrastructure for enterprise customers bringing their own data.
  • Develop playbooks and templated Terraform so private cloud deployments can be stood up as a routine, approximately one-day operation.
  • Improve observability signal-to-noise ratio, with health monitors covering every endpoint and job and actionable alerts.
  • Schedule and conduct database restoration drills and provider-outage dry runs, and produce post-mortems to improve diagnosis.
  • Follow through on SOC 2, NIST AI framework, and EU Cyber Resilience commitments, supporting faster enterprise security reviews.
  • Find and execute opportunities to shift workloads between providers to reduce p95 latency and cost.
  • Design the incident response and on-call setup, including sensible alerting, service-level agreement tracking, structured post-mortems, and an eventual sustainable rotation.

Desired Qualifications

  • Familiarity with GCP.
  • Experience writing about, speaking about, or building projects demonstrating the use of AI coding agents.
  • Experience with DataDog or an equivalent observability stack.
  • Ability to work from the Oakland office or remotely with time-zone overlap between GMT and GMT-8, and travel for quarterly in-person offsites.

About the Company

Elicit

AI-powered literature review and data extraction

Elicit is an AI-powered research assistant that helps scientists, students, and professionals conduct literature reviews. Users ask natural-language questions and the system uses semantic search over a database of 125+ million papers to find relevant studies, then extracts data and creates organized tables or summaries, with support for uploaded PDFs. It grounds outputs in verifiable sources to reduce AI hallucinations, a key factor for high-stakes fields, and serves a range of users from individuals to large organizations. Its goal is to scale up good reasoning by making research faster, more efficient, and more reliable.

Company size
51-200
Company stage
Series A
Total funding
$31M
Headquarters
Oakland, CA
Founded
2023

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