As the AI/Automation Engineer at Synapse Labs, you will design and build the systems that power our internal automation and marketing intelligence initiatives. Your work will eliminate manual bottlenecks and transform search, AI, and competitive data into structured, actionable insights.

You will partner with our strategy team to translate business ideas into scalable backend systems, data pipelines, and cloud-based workflows. This is a foundational engineering role within Synapse Labs. You will have significant autonomy in shaping architecture decisions and establishing engineering standards as we scale our internal automation capabilities.

Core Responsibilities

• System Design & Development: Design and implement backend systems that automate marketing workflows, including search intelligence tracking, competitor monitoring, reporting automation, and operational tooling.
• Data Engineering: Build and maintain structured data pipelines that ingest, transform, and store large volumes of search, AI, and performance data. Design clean schemas and optimize queries for performance and cost efficiency.
• Infrastructure as Code & DevOps: Ensure that deployments are automated, environments (Dev/Stage/Prod) are consistent, and the codebase is stable and reliable.
• Cloud & Deployment: Deploy and manage automation workloads in a cloud environment (GCP preferred). Improve CI/CD processes, containerization practices, and environment structure (dev/staging/production).
• Reliability & Monitoring: Implement logging, retry logic, alerting, and error handling across automation workflows.
• Technical Collaboration: Work closely with marketing strategists to convert evolving business requirements into durable technical systems. Provide architectural input on new automation initiatives.

Technical Skills Required

• Primary Language: Strong proficiency in Python. Experience building production-grade backend systems.
• Data & SQL: Deep knowledge of SQL and experience working with cloud data warehouses (BigQuery preferred). Ability to design scalable data models and optimize large datasets.
• APIs & Automation: Experience integrating third-party APIs, handling rate limits, managing concurrency, and designing reliable ingestion workflows.
• Cloud Infrastructure: Hands-on experience deploying and maintaining serverless or containerized workloads (GCP, AWS, or similar). Familiarity with Docker and CI/CD pipelines.
• Observability: Experience implementing logging, monitoring, and alerting in production systems.

Preferred Background

• Education: B.S. in Computer Science or Engineering. Equivalent practical experience building production-grade systems is equally valued.
• The "Hybrid" Mindset: You don’t need to be a marketing expert, but you should be curious about how search engines work and how data drives business decisions. You’re comfortable partnering with non-technical stakeholders and translating business problems into clean technical solutions.
• Systems-Oriented Problem Solver: You see manual, repetitive processes as engineering opportunities. If a spreadsheet takes 10 hours to complete, your instinct is to automate it in 10 seconds.