Job DescriptionThe role:\\n
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Are you interested in applying software to improve US manufacturing?
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Would you love to be part of a team that uses Software and AI to transform legacy manufacturing companies?
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This startup have raised $20m so far and are acquiring smaller, typically family-run manufacturing businesses and transforming how they operate. As well as improving each business individually, they're building a software and AI platform that can eventually be rolled out across every company they acquire, increasing the efficiency of multiple machine shops and, ultimately, increasing production outputs.
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That could mean spending time understanding how something currently works on the factory floor, figuring out how software can improve it, designing the system, building it and then seeing it deployed into a real manufacturing environment.
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The person:\\n
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This Software Engineer role would ideally be suited to strong generalist engineers rather than someone tied to a particular language or framework. You might lean backend, full-stack or ML, but you should be comfortable taking ownership beyond a narrow part of the stack.
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The ideal background\\n
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\\n - Roughly 3-5 years of software engineering experience
\\n - Experience in a high-bar engineering environment
\\n - Strong fundamentals and system design ability
\\n - Comfortable owning ambiguous problems from idea through to production
\\n - Genuine interest in startups
\\n - Genuine interest in physical tech, manufacturing or American industrial growth
\\n - - essential for security vetting purposes
\\n - Based in or willing to relocate to NYC
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The package:\\n
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$200k-$250k base + equity
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This role is particularly interesting for someone coming from a strong tech company who wants to get closer to the problems they're solving: small team, huge ownership, very little bureaucracy, and software that has a direct impact on things being built in the physical world.
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Please note: candidates must be willing and able to travel around manufacturing sites across the US, approximately 20% of the time
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