Software Engineer

What You'll Do

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

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

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

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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

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

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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

What Sets You Apart

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

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

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

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

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

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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

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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

Technical Requirements

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4+ years of software engineering experience

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Strong proficiency in Python and/or modern JavaScript/TypeScript

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

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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

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Experience designing and implementing APIs and data models

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Experience with data processing and analytics systems

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Comfort with agile development methodologies

Preferred Qualifications

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

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

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Fullstack experience (frontend and backend)

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