Founding Systems Engineer

Ionworks is building next-generation battery simulation and optimization tools, powered by PyBaMM and modern cloud infrastructure. Our mission is to accelerate the development of better batteries, enabling faster electrification, safer devices, and more sustainable energy systems. The backend you build will directly support large numbers of simulations and help engineers design real-world batteries faster and more efficiently. As our first dedicated backend and systems hire, you will own the architectural backbone of our platform. You will work closely with the founders and a small, high-velocity engineering team to build reliable APIs, optimize performance across the stack, and design robust data systems for numerical simulation workflows. This role is for someone who practices "vibe engineering": using LLMs and coding agents seriously and responsibly, while staying fully accountable for the quality of the systems you ship. What you will do Build and evolve backend services using FastAPI, async Python, and Supabase or Postgres Design clear, stable API contracts for simulation, optimization, and data workflows Architect schemas, transactions, indexing strategies, and migrations for evolving scientific workloads Diagnose and optimize performance end to end, including query plans, network latency, concurrency, and resource limits Build resilient async job flows for running and tracking simulation pipelines, including distributed or queued workloads Implement observability across the backend: tracing, logging, metrics, error handling, and recovery strategies Use LLMs and coding agents to accelerate work while keeping a strong human review loop, tests, and code quality Collaborate directly with the founder and compute engineers to shape system architecture, engineering culture, and technical standards Influence core technical decisions and establish backend best practices as we scale from a handful of customers to many more Requirements 4+ years of experience building production backend systems in Python (FastAPI, Flask, or similar frameworks) Strong SQL and Postgres experience, including schema design, query optimization, and migrations Experience debugging complex systems: async concurrency, database connection pools, distributed or multi-service flows Familiarity with cloud environments (for example AWS, GCP, Porter), Docker, and CI/CD pipelines Comfortable owning systems that serve real users: you care about correctness, performance, observability, and security Experience using LLM tools in a serious way: as helpers and agents that you supervise, not as one-click generators Clear communication and the ability to work autonomously in an early-stage, fast-moving environment Nice to have Experience with distributed or parallel systems such as Celery, Ray, Dask, or HPC queues Background in numerical computing, scientific Python, or simulation workflows Experience with multi-tenant architectures and dataset isolation strategies, including row-level security Familiarity with TypeScript or React, or building well-defined API contracts for frontends Interest in energy storage, electrification, or engineering tooling Contributions to open-source projects, especially PyBaMM or scientific or infrastructure tooling What we mean by "vibe engineering" We are not looking for "vibe coding" where you paste a prompt into an LLM and ship whatever looks like it works.

By "vibe engineering" we mean an approach where you:

Use LLMs and coding agents to speed up work, but stay fully responsible for the architecture, quality, and maintainability of the code

Lean on strong engineering practices that make agents more effective:

Automated tests, ideally written first or early Clear high-level plans before coding Documentation that explains how systems fit together Clean Git history, small commits, and the ability to bisect and debug CI, linting, formatting, and preview environments that keep changes safe

Treat agents like very fast but very literal collaborators:

You give them clear specs and success criteria You review their output critically You use manual QA and exploratory testing to catch edge cases Use research and judgment to choose approaches before writing code, instead of asking an LLM to "just build something" Maintain a sense for which tasks are safe to delegate to AI and which require manual care

• In other words: you use AI tools to expand your impact, not to avoid thinking. You stay an engineer, not a prompt operator.

Why join Ionworks?

• Work that matters: your engineering directly accelerates electrification and next-generation battery development.
• Early ownership: shape our core architecture, engineering culture, and have meaningful equity.
• Greenfield systems: build infrastructure that supports advanced simulation pipelines used by leading R&D teams.

Tight-knit team: collaborate with world-leading experts who deeply care about the tools they are creating. Innovation culture: help define a modern cloud platform at the intersection of physics simulation, data systems, distributed compute, and serious AI-assisted development. Benefits Unlimited PTO 401(k) plan with employer match Medical, dental, and vision coverage Remote-first work environment Paid parental leave Home office and hardware budget