Software Engineer

Build and ship: Implement features with an eye toward scalability, performance, and maintainability

Ship fast, learn fast: Work in a rapid-iteration environment where prototypes and customer feedback shape what gets built next

Solve real problems: Partner with product, design, and occasionally customers to translate needs into practical solutions

Leverage AI fluently: Use AI coding assistants, agentic workflows, and LLM-based tools as a daily part of how you build — and contribute ideas for how the team can do more with them

Uphold quality: Practice solid code hygiene, testing, and documentation; help keep the bar high even as velocity increases

Contribute ideas: Share input on technical approaches, tooling choices, and how we evolve the platform — senior engineers own architectural decisions, but good ideas are welcome from anyone

Technical depth: Strong engineering fundamentals and real depth in your core areas

Product sense: You think about how your technical choices affect users and the business, not just the code

Ownership: You can take a feature from concept through deployment without heavy hand-holding

Collaborative instincts: You work well with product, design, and other engineers, and you give and take feedback well

Pragmatic problem-solving: You balance technical elegance with practical constraints and shipping on time

AI-enhanced workflow: You've already built AI assistants into your development loop — Copilot, Cursor, Claude Code, or similar — and you have opinions on where they help, where they don't, and how to keep quality high while using them

AI implementation experience: You've built production features that use LLMs, ML models, or agentic workflows, and understand the tradeoffs of choosing the right tool for a given problem

4+ years of software engineering experience

Strong proficiency in Python and/or modern JavaScript/TypeScript

Experience with cloud infrastructure (AWS, GCP, or Azure) and DevOps practices — we use AWS managed with CDK

Hands-on experience building features that integrate LLMs, ML models, or generative AI — choosing the right tool, implementing it in production, and managing its behavior over time

Experience designing and implementing APIs and data models

Experience with data processing and analytics systems

Comfort with agile development methodologies

Experience in aerospace, energy, manufacturing, or other hardware-integrated software environments

Exposure to IoT, time-series data, telemetry, or industrial control systems

Fullstack experience (frontend and backend)

Track record of adopting or deploying AI tooling inside a team (not just using it individually)