Senior Software Engineer (Reliability)

Berry AI builds AI-powered operations platforms for QSR restaurants — drive-thru analytics, loss prevention, and store management tooling deployed across thousands of locations across the US — and growing. To help us scale with high reliability, we are seeking a Senior Software Engineer. The role is focused on architecture review, monitoring, and production-readiness.

What you'll work on

• Analyze and optimize our edge-cloud architecture using modern monitoring tools, continuously driven by data insights.

• Formulate and execute a robust monitoring strategy tailored for our thousands of edge servers and cameras.

• Establish standards and guide our software development workflows to mitigate or clarify risks before they arise.

• Contribute to the operational backbone — Ansible playbooks, CI/CD pipelines, and observability — that lets us deploy, monitor, and operate the fleet.

• Strengthen our on-call practice — leading postmortems, sharpening alerting and runbooks, and turning incidents into durable fixes.

You're a strong fit if you have

• Over 5 years of experience delivering production software, with deep expertise in architecture design and conducting code reviews.

• Experience mapping out complex data flows within massive codebases.

• Disciplined practice of DevOps and SRE — metrics, structured logging, tracing, runbooks.

• Proven ability to validate system reliability by designing and executing load tests, stress tests, and chaos engineering experiments.

• Strong cloud and system-design fundamentals — designing for AWS/GCP/Azure at scale (compute, storage, autoscaling), clean REST/GraphQL contracts, and async/event-driven pipelines.

• Strong data modeling experience at scale — relational and NoSQL databases, dimensional modeling for analytics, and robust ETL/ELT pipeline design.

Bonus points

• Experience managing and deploying to massive fleets of on-premise or edge devices (IoT).

• Hands-on experience working with open-source software, including modifying, extending, and improving existing projects—not just using them.

• Experience with real-time/streaming systems (RTSP, WebRTC, MediaMTX).

• Experience Kernel performance insights - scheduling, context switching, hardware acceleration.

• Deep knowledge of GPU analytics and performance optimization for edge-based AI deployments.

Our engineering culture

Small team, high ownership, fast feedback from engineer teams — and the operational rigor to make that velocity sustainable. We hack. We drive. We deliver. No technical limitation stands in our way — until we turn what seemed impossible into something real.

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Interview Process

1. Online (Google Meet)

   1. Team Lead Interview (0.5 - 1 hr)

2. Onsite

   1. Technical Interview (2.5 hrs)

   2. CEO & VP Interview (1.5 hrs)

   3. PM/Engineer Interview (0.5 hr)

   4. HR Interview (0.5 hr)