Senior Software Engineer (Full-Stack / Backend)

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. We're hiring a Senior Full-Stack Engineer who ships product features fast and brings the operational discipline to run them reliably.

What you'll work on

• Ship product features end-to-end across our hybrid cloud-edge architecture — from edge services through cloud APIs to the daily analytics restaurant operators rely on.

• Build out our data platform — data ingestion pipelines from thousands of stores, dbt models, and the warehouse powering our dashboards.

• Collaborate with our AI engineers to turn their models into product features — clean APIs, intuitive UIs, and the insights they unlock.

• 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

• 5+ years shipping production web applications, with deep experience in TypeScript (React/Vue/Angular) and Python (FastAPI/Django/Flask).

• A track record of owning features end-to-end — schema design, API contracts, frontend state, deployment, and post-launch iteration.

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

• Deep SQL and data-modeling experience — PostgreSQL at scale, dimensional modeling for analytics, and dbt or equivalent transformation tooling.

• Disciplined practice of DevOps and SRE — metrics (Prometheus, vmagent, Grafana), structured logging, tracing, runbooks.

Bonus points

• Experience managing fleets of on-prem/edge devices.

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

• Experience with Apache Superset or other data-platform / analytics surfaces.

Our engineering culture

Small team, high ownership, fast feedback from customers — and the operational rigor to make that velocity sustainable. Modern AI tooling — LLMs, coding agents, agent-driven workflows — is a normal part of how we work, and you're encouraged to push on what these tools can do.

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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)