Senior Applied AI Engineer

QuEra Computing•Boston, MA

About The Position

QuEra is establishing a new AI Engineering team to empower all company groups in leveraging AI. We are seeking senior founding engineers to define the team's standards. In this role, you will transform high-value concepts from company-wide AI workshops into deployable tools. You will build LLM- and agent-powered software to accelerate and democratize processes that are currently slow, costly, and require specialized expertise, covering areas from machine build and bringup to daily engineering tasks. This senior, highly autonomous position is intended for an expert in the field. You will be responsible for projects from inception to completion, establishing the technical patterns for the team's development, and elevating the quality of AI software engineering across QuEra. Your deep, modern AI application-engineering expertise will complement our existing strengths in physics, controls, and hardware, and you will play a key role in rapidly building this talent within the company. Prior experience in quantum computing is not a prerequisite.

Requirements

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.

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.

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.