Job description

Role Overview QuEra is standing up a new AI Engineering team to help every group in the company put AI to work. We are hiring senior founding engineers to set its bar. In this role, you will turn the highest-value ideas from our company-wide AI workshop into real, deployable tools, and build LLM- and agent-powered software that makes our slow, expensive, and expertise-gated steps faster, cheaper, and more accessible — from machine build and bringup to everyday engineering. This senior, high-autonomy role is designed specifically for someone at the top of their craft. You will own projects end to end, set the technical patterns the team builds on, and raise the bar for how AI software is built across QuEra. You bring deep, modern AI application-engineering expertise that complements our in-house physics, controls, and hardware strength — and you help us build that talent quickly. A background in quantum computing is not required. Key Responsibilities Build and ship internal AI tools and features end to end — from a rough idea to a deployed and maintained product. Set the technical direction and reusable patterns the AI Engineering team builds on and raise the engineering bar across the company by example. Design and deploy LLM and agentic systems — retrieval, tool use, orchestration, and evaluation — with sensible guardrails and humans in charge where it matters. Partner with engineering, hardware, operations, and other teams to turn their ideas into working tools they can own. Create shared components, templates, and infrastructure that let other teams build faster. Right-size models and optimize for cost, latency, and reliability; instrument usage and quality. Mentor teammates and help grow the group’s talent fast; model strong engineering practice — clear specs, real documentation, tests, and honest estimates. Drive work to completion independently — and, when needed, make the call to stop the work early and cleanly. Required Qualifications Deep software-engineering expertise and a strong track record of shipping and operating production software at scale (primarily Python), with excellent testing, code review, CI/CD, and documentation habits. Substantial hands-on experience building LLM-powered applications: prompt and agent design, tool/function calling and MCP-based integrations, retrieval-augmented generation, and rigorous evaluation. Proven work with agentic systems and agentic coding workflows — designing or integrating agents that take actions against real systems safely. Architectural judgment: able to design systems others build on, and deliver end to end across backend services, APIs, light front-end, and deployment. High autonomy and ownership — thrives in ambiguity and reliably carries a project from idea to production with little oversight. A demonstrated record of impact you can point to. Publications and notable open-source or research contributions count, but we weigh shipped software and fast, real-world delivery over research output. A force multiplier for those around you: clear communicator, natural mentor, and able to translate a non-expert’s need into a working tool. Preferred Qualifications LLMOps / MLOps: model serving, monitoring, evals, and cost/latency optimization (right-sizing models). Data and ML engineering: pipelines, embeddings, and vector databases; fine-tuning or adapting models. Deep-learning frameworks (e.g., PyTorch) and experience building or adapting generative models such as diffusion or image generation — useful if the team takes on more model-building work over time. Front-end / UX for internal tools (e.g., React / TypeScript). Experience with modern coding agents and the broader agent-tooling ecosystem. Kubernetes, Docker, and CI/CD tooling. Exposure to scientific, hardware, lab-automation, or other complex operational environments. What Success Looks Like You ship useful tools into production quickly, and teams actually adopt them. The components you build get reused by others, rather than becoming one-offs. Your work is well-documented, tested, and dependable. You operate independently and reliably move projects to done — or to a clean stop. The approximate base salary range for this position is $175,000 - $210,000. We consistently monitor external market data and update base salary ranges accordingly. We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience. In addition to our base salary offerings, we also provide equity grants for all new hires. QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. #LI-DA1

Requirements

Must-haves

Build and ship internal AI tools and features end to end — from a rough idea to a deployed and maintained product.
Set the technical direction and reusable patterns the AI Engineering team builds on and raise the engineering bar across the company by example.
Design and deploy LLM and agentic systems — retrieval, tool use, orchestration, and evaluation — with sensible guardrails and humans in charge where it matters.
Partner with engineering, hardware, operations, and other teams to turn their ideas into working tools they can own.
Create shared components, templates, and infrastructure that let other teams build faster.
Right-size models and optimize for cost, latency, and reliability; instrument usage and quality.
Mentor teammates and help grow the group’s talent fast; model strong engineering practice — clear specs, real documentation, tests, and honest estimates.
Drive work to completion independently — and, when needed, make the call to stop the work early and cleanly.
Deep software-engineering expertise and a strong track record of shipping and operating production software at scale (primarily Python), with excellent testing, code review, CI/CD, and documentation habits.
Substantial hands-on experience building LLM-powered applications: prompt and agent design, tool/function calling and MCP-based integrations, retrieval-augmented generation, and rigorous evaluation.
Proven work with agentic systems and agentic coding workflows — designing or integrating agents that take actions against real systems safely.
Architectural judgment: able to design systems others build on, and deliver end to end across backend services, APIs, light front-end, and deployment.

Nice-to-haves

LLMOps / MLOps: model serving, monitoring, evals, and cost/latency optimization (right-sizing models).
Data and ML engineering: pipelines, embeddings, and vector databases; fine-tuning or adapting models.
Deep-learning frameworks (e.g., PyTorch) and experience building or adapting generative models such as diffusion or image generation — useful if the team takes on more model-building work over time.
Front-end / UX for internal tools (e.g., React / TypeScript).
Experience with modern coding agents and the broader agent-tooling ecosystem.
Kubernetes, Docker, and CI/CD tooling.
Exposure to scientific, hardware, lab-automation, or other complex operational environments.
What Success Looks Like
You ship useful tools into production quickly, and teams actually adopt them.
The components you build get reused by others, rather than becoming one-offs.
Your work is well-documented, tested, and dependable.
You operate independently and reliably move projects to done — or to a clean stop.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonci/cddesign systemsmlreacttypescriptkubernetesdocker

Platform: greenhouse
Effort: quick