Senior Software Infrastructure Engineer

Description

About the company

The company builds industrial AI robots that are working in the real world today. Our robots automate mission-critical warehouse labor end-to-end, starting with e-commerce order packaging. We deploy fast, learn from real production data, and iterate aggressively. We already have a quickly growing number of revenue-generating robots operating in U.S. warehouses, and are now scaling towards thousands of deployments over the next few years. Our mission is simple: build the world's most useful and deployable robot.

WARNING: Robotics is not for the faint of heart. There are no silver bullets here. It is tiresome, unglamorous, and at times brutal work. The only way out is through, and the only way through is treacherous and unknown. Success will require teamwork - so we are building a world-class team to tackle some of the most exciting problems out there. We hope you join us.

The company is looking for a Senior Software Infrastructure Engineer to design and implement industry standard company data infrastructure and tooling, along with airtight information security and architecture, to enable multiple mission-critical projects for the company’s long-term growth. As Senior Infrastructure Engineer, you'll own the company's data infrastructure and information security architecture end to end: auditing what exists today, closing the gap to industry-standard practice within six months, and building the authZ/authN foundation that makes AI tooling and enterprise customers trust us with their data. You'll also own the platform that lets engineers and vibe-coders across the company get safe, compliant data access without waiting on you for every request. This is an in-person NYC role with direct line of sight to top-line business goals.

Responsibilities

• Audit existing authn/authz infrastructure and produce a gap analysis against industry standards
• Design and execute a roadmap to bring infrastructure to industry-standard within 6 months
• Drive SOC2 compliance workstream — controls, evidence, remediation; turn compliance requirements into enforceable platform controls
• Build scalable systems/tooling for onboarding new data sources
• Design and implement authorization architecture that securely enables AI tooling across the company
• Support software engineers and vibe-coders so everyone can ship code safely and quickly
• Partner with engineering leadership on architecture decisions that support enterprise-market expansion
• Bonus Points
• You've built a comprehensive authentication and authorization system at another company
• You have strong opinions about the best way to implement RBAC (and its limitations)
• Expected Compensation
• $160,000 - $250,000/annual salary + equity + benefits
• This compensation range may be inclusive of several career levels at the company and will be narrowed during the interview process based on the candidate’s experience and qualifications. Adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description.
• Equal Employment Opportunity Statement
• We are an Equal Opportunity Employer. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to creating a diverse and inclusive environment and encourage applicants from all backgrounds to apply.
• Experience: 5+ years
• Visa: US citizen/visa only

Requirements

• You have 7+ years in infrastructure/security engineering, with real ownership of authn/authz systems in production
• You're proficient in Python; a software generalist who knows when to build vs. buy
• You've led or heavily contributed to a SOC2 (or equivalent) compliance effort — you know what auditors actually look for
• You're comfortable auditing legacy systems and making the call on what to fix, replace, or leave alone
• You have experience designing data infrastructure that scales to onboard new data sources without re-architecting each time
• You're dangerous with LLMs because you know when they're wrong; you've thought hard about misbehaving agents and what it takes to secure AI tooling
• You can look at a backlog of "we need access to X" requests and pick the highest-leverage platform investment
• You have a bias toward shipping durable systems over quick patches; you document decisions so others can build on them