Senior Software Engineer; AI Infrastructure

This role is open to candidates in Boston, New York, Washington, D.C., Chicago, LA, Silicon Valley and San Francisco.

The Cornerstone Research Data Science Center is seeking a Senior Software Engineer to design, build, harden, and scale infrastructure that powers AI applications across the firm. This is a hands‑on role within a versatile software engineering team that operates with a high degree of autonomy and scope.

You will co‑own the platform layer beneath the firm’s AI tooling, including inference gateways, containerized environments, and observability tools. The work is a mix of building new capabilities and hardening existing infrastructure in a domain where confidentiality, isolation, and reliability are critical.

This role is ideal for an engineer who enjoys building products and services for other developers: someone equally comfortable standing up a new service, tracing a request across the platform to determine why it was slow, figuring out why a container works locally and fails on the host, and replacing a manual deployment step with a pipeline. You will work closely with engineers who own the platform layer and developers who build on it.

• Platform Ownership: Design, build, and operate the systems and shared infrastructure that AI applications at the firm depend on.
• Observability & Reliability: Own metrics, logs, and traces for the platform. Instrument services, build dashboards and alerts, and use telemetry to diagnose and resolve performance and reliability issues.
• Hardening & Operations: Improve the resilience, security posture, and operational maturity of existing systems.
• Developer Enablement: Provide the tooling, environments, and deployment paths that let application developers ship AI agents and workflows onto the platform quickly and safely. Treat internal developers as your users.
• Collaboration: Partner with engineers, data scientists, product managers, and corporate departments (Security, IT) to understand requirements, communicate tradeoffs, and deliver platform capabilities that fit how the firm works.

• Experience: BA/BS with 4+ years of professional software, platform, infrastructure, or Dev Ops engineering experience, OR a Master or PhD in a quantitative field (e.g., Computer Science, Math, Economics) with 2+ years of industry experience.
• Engineering Mindset: Strong fundamentals in system design, with the ability to build reliable systems that handle sensitive data with high reliability.
• Technical Versatility: Demonstrated ability to work across tech stacks and pick up new languages or frameworks quickly
• AI‑Native Development: Experience using AI‑powered coding assistants to accelerate development; strong interest in staying at the forefront of AI‑enabled engineering.
• Agentic Tooling: Familiarity with building AI agents (e.g., using the Claude Agent SDK), and with what it takes to run agents safely in sandboxed environments.
• Containers: Deep, practical experience with environment isolation concepts and container solutions such as Docker, Podman, or Bubble wrap
• Observability: Hands‑on experience with metrics, logging, and distributed tracing, ideally with the Grafana ecosystem (Grafana, Loki, Tempo, Mimir, Alloy) and Open Telemetry.
• Communication: Ability to clearly articulate technical tradeoffs, document systems well, and explain progress to both technical peers and non‑technical stakeholders.

• Reverse Proxies & Gateways: Experience configuring and operating reverse proxies or API gateways (e.g., Nginx): routing, TLS, authentication, rate limiting, and request/response handling.
• Networking & Service Connectivity
  :
  Solid grasp of how services find and talk to each other across hosts and networks, including…