Engineering Manager

Before you apply

• We're direct about how we work. Read this section first.
• We value leaders who know what they don't know, and say so. If you're honest about your gaps, we'll invest in closing them. If you hide weaknesses or oversell your experience, you won't last here.
• This role requires hard calls: on architecture, on timelines, on people. You'll need to give difficult feedback, exit under-performers, and push back on unrealistic asks. If you avoid conflict or defer every decision upward, this isn't the right fit.
• We move fast. Growth happens outside comfort zones. If this sounds harsh, we're probably not a fit. If it sounds like the environment you've been looking for, keep reading.

The role

Allerin builds AI and IoT products: edge to cloud video intelligence, computer vision systems, and data platforms. We also partner with select clients to solve their hardest technical problems. You'll do both.

The engineering work here includes a production Rails platform in parking management, covering payments, permits and citations, with roughly 160 models, 231 controllers and five API surfaces, alongside the vision and IoT product line. None of it is greenfield. You would lead 6 to 10 engineers across that work, including the Rails backend lead and the engineers building alongside them.

What you'll own

• Technical Leadership: Translate product vision into technical goals, review design docs and PRs, raise the bar on code quality and observability
• People and Delivery: Lead 6 to 10 engineers, provide candid feedback and career coaching, balance velocity with technical debt paydown
• Process and Culture: Champion continuous delivery, foster blameless incident culture, collaborate with Product and UX
• Strategic Impact: Partner with the CTO on capacity planning and build versus buy decisions, monitor engineering KPIs and act on what metrics reveal

The review bar for AI-written code

Your engineers use AI coding tools. That has moved the hard part of the job, and it is now yours rather than theirs. Reviewing generated code takes longer than reviewing a colleague's, because it arrives with clean syntax and consistent naming, so the signals that used to make bad code obvious are gone. What hides underneath is a misunderstood requirement or an edge case nobody considered.

At a team of this size that becomes a throughput problem before it becomes a quality problem. More changes arrive, each one takes longer to review properly, and the pressure to wave things through lands on the person with the deepest knowledge of the system. Setting that bar, holding it, and knowing from evidence rather than instinct whether it is holding, is a real part of this role. If you have opinions about it already, we want to hear them.

Must-have experience

• 8+ years in software development, with 4+ years leading engineering teams
• Built and scaled teams of 5+ engineers. Hired, coached, and exited under-performers when necessary.
• Shipped SaaS or data intensive products in cloud environments (AWS/GCP/Azure)
• Deep understanding of CI/CD, automated testing, and modern DevOps practices
• Strong communication. Can push back on scope creep while keeping morale high.

Nice to have

• Experience with AI/ML pipelines or real time computer vision workloads
• Familiarity with multi tenant SaaS security (RLS policies, JWT claims, zero trust access)
• Exposure to SOC 2 or HIPAA compliance environments

What we offer

• Real authority over technical direction, not a title that has to ask
• A short path from a decision to production, without approval theatre
• A team small enough that what you change is visible, on systems that genuinely run
• Investment in your growth if you're honest about where you need it

Apply through the form. We ask questions designed to surface how you think, not how well you can prompt Generative AI tools like ChatGPT. A human reads every application, and answers that read as model-generated are tested in depth in the interview, where they do not survive. Everything you write will be discussed there.