Sr. SDE (Email)

How we work

• Iterate fast: Go from idea to prototype to production in days, not quarters. Ship small, measure, learn and improve.
• Use AI well: Use AI across the whole cycle (design, coding, testing, reviews and debugging) to move faster without lowering the quality bar, and pick the right model for each job based on quality, latency and cost.

About the Role:

The traditional model of siloed product managers, designers and engineers is too slow for AI-native development. At HighLevel, every engineer owns a product outcome end to end, from idea to prototype to production to scale. You will operate as a Team of One. With AI tooling and strong engineering infrastructure, one engineer can now build what used to take a whole squad. You won't be implementing tickets. You'll own the product.

Responsibilities

Feature & Module Ownership 

Translate business goals into working, deployable systems. 

Propose architecture, data models, workflows and implementation plans for your modules, then refine them with Tech Leads and Engineering Managers before you build. 

Own modules end to end: design, release, post-launch stability and ongoing improvements. 

Design clean, versioned, well-documented APIs as a core part of every feature, so the platform can be integrated wherever it's needed. 

Rapid Prototyping & Iteration 

Turn ambiguous ideas into working demos quickly, using AI to build and iterate fast.

Validate through experiments and real user feedback, then iterate to production quality.

Drive feature attribution and metrics visibility so every release is measurable. 

Scale, Performance & Reliability 

Build and maintain the Email ecosystem, designing for scalability, reliability and data integrity at 40M+ emails/day. 

Evolve our sending infrastructure, including IP pool management, dedicated IPs and domains, and shared domain pools, to protect sender reputation and maximize inbox placement. 

Build robust error handling: idempotent processing, retries with backoff and graceful degradation. 

Optimize frontend rendering, component architecture, lazy loading and state management. Tune backend queries, caching (Redis/CDN) and latency at scale.

Design data models and analytics on ClickHouse, MongoDB and Firestore.

Reason through token consumption, inference cost and infrastructure tradeoffs when integrating AI and LLMs. 

Engineering Quality & Security 

Practice test-driven development and hold a high bar for automated test coverage.

Build securely by default: authentication, secrets management and safe handling of customer data and PII. 

Operational Excellence 

Instrument systems with monitoring and telemetry. 

Build evaluation loops for continuous quality improvement. 

Automate workflows that increase engineering velocity. 

Keep documentation clear and repeatable. 

Requirements

4+ years building and running production systems, including owning them when they broke: the on-call, the incident and the fix. 
Node.js (TypeScript) at scale: You've shipped and operated high-throughput backends (queues, workers, retries, millions of events a day) and built real product UIs in Vue.js (or React). 
Systems that grew: You've designed or re-architected a system for 10x growth and can explain the tradeoffs you made in queues, caching, partitioning and storage. 
Data you can trust: You've built pipelines where losing or duplicating a record wasn't acceptable, using idempotency, deduplication, reconciliation and error handling that fails loudly, not quietly. 
Data modelling & analytics: You've modelled data for transactional and analytical workloads, and made queries fast over billions of rows in ClickHouse (or BigQuery, Snowflake or similar). 
Cloud in production: You've run services on GCP orAWS (Kubernetes, Pub/Sub or SQS, managed databases, IAM). 
Quality & security by habit: You write tests first or alongside code (TDD), and treat auth, secrets, PII and abuse prevention as part of the design, not a review step. 
AI in the product: You've shipped at least one AI or LLM-powered feature to real users and can walk through the model choice, prompts, evals, cost and latency. 
AI in your workflow: You use AI coding tools every day, go from idea to working prototype in hours or days, and still own and stand behind every line that ships. 
Problem solving & ownership: You take vague problems, break them down and get to a working answer without waiting for a ticket. 

Bonus Points

Experience with Go (Golang). 
Experience in the email domain: email rendering, templates, SMTP/ESPs, or deliverability (IP warm-up, sender reputation, SPF/DKIM/DMARC). 
Experience with data pipelines, analytics or telemetry. 
Strong UX and front-end sensibility. 
A portfolio or GitHub showing deployed systems.