• Own Bazel build configurations, custom rules, and language toolchains for C++, Java, Python, and TypeScript. • Operate and harden the Buildfarm remote-execution service: monitoring, incident response, performance tuning, and capacity planning. • Build automation and services (primarily using Python) to integrate Bazel with CI and developer workflows. • Maintain developer and test tooling (Github Actions workflows and runners, development environments) and the automation that keeps them reproducible. • Produce documentation for future maintainers and concise runbooks for developers.

• Lead the design and development of advanced threat research platforms and prototypes, focusing on automation and scalable intelligence workflows • Architect and code solutions for collecting, processing, and analyzing diverse threat data sources, including telemetry, commercial feeds, and OSINT • Conduct in-depth research on emerging threat actors, tactics, techniques, and procedures (TTPs), including dark net intelligence gathering, and produce actionable reports for stakeholders • Engineer and automate the intelligence cycle, continuously improving processes for detection, alerting, and incident enrichment using SIEM, SOAR, and EDR technologies • Mentor and guide team members in advanced coding practices, threat research methodologies, and engineering best practices • Develop and present technical briefings, research papers, and position documents to executive leadership and external partners

• You'll build a comprehensive system that runs fast, is easy to use, and supports quickly building new evals: • Speed: You’ll build a lightning-fast basic evals infrastructure that schedules tasks to introduce practically no latency; and then you’ll figure out clever ways to solve the fundamental sources of latency (building a version of Elicit, running it on a query, and evaluating it using LMs) • Interfaces: ML engineers need evals to kick off automatically on relevant commits, with results they can see at a glance and drill into. Product managers need dashboards showing performance over time and what's going wrong in production. • Architecture: Your code must be well-architected so other team members and ML engineers can understand and build on it. An engineer starting on a new feature should be able to quickly add examples and run an eval. • We need to evaluate how well Elicit actually helps with decision-making in pharma, not just measure what's easy to measure. This requires encoding real knowledge about how pharma customers make decisions (for example, choosing appropriate gold standards). • You'll provide appropriate statistical tests and confidence intervals so we can trust our results. • In a typical month, expect to spend: • 60% working on the core eval platform • 15% working closely with the evals team to build and improve specific evals (e.g., an eval of our paper search within our systematic review flow) • 10% mentoring our evals engineering intern • The rest on learning how people interact with the eval system so you can make it work better for them, and understanding what our users want from Elicit so evals measure what matters

• Administration, operation, and maintenance of Postgres environments • Design, implementation, and optimization of database architectures • Planning and executing installations, upgrades, migrations, and patching of Postgres DB • Monitoring performance and tuning Postgres databases • Implementing backup & recovery strategies • Troubleshooting database issues, identifying root causes, and implementing corrective actions • Ensuring availability, reliability, and high performance of database systems • Supporting high availability and disaster recovery (HA/DR) configurations • Creating and maintaining system and process documentation • Collaboration with development, infrastructure, and operations teams • Ensuring adherence to security policies, compliance requirements, and audit standards • Contributing to automation initiatives and continuous service improvement