Senior Backend Software Engineer

Spring Labs Holdings, Inc.•Marina del Rey, CA

About The Position

At Spring Labs, we’re building a unified platform that powers end-to-end workflow automation for customer support and compliance teams in financial services. Our AI-native system interprets customer intent, understands regulatory nuance, and orchestrates complex processes through configurable automation — helping banks and fintechs scale service, reduce manual work, and stay audit-ready without adding headcount. We’re looking for a Senior Backend Engineer who can own core services end-to-end: ingestion, processing, orchestration, and delivery at scale. You’ll work closely with product, data science, and infra to evolve a system that processes high-volume, high-stakes customer interaction data—reliably and fast. If you're passionate about tackling complex problems with a team of accomplished professionals, and the opportunity to learn from domain experts excites you, we’d love to hear from you!

Requirements

3+ years of professional software engineering experience, with a strong backend focus
Strong proficiency in Python; experience with distributed systems is a plus
Hands-on experience designing and maintaining RESTful APIs and backend services
Experience working with data pipelines and processing frameworks
Exposure to event-driven systems, queues, or workflow engines
Comfortable deploying and operating services in production (monitoring, debugging, scaling)
Familiarity with databases (relational and/or NoSQL) and data modeling
Experience integrating ML/LM models into production systems
Strong engineering judgment—knows when to build, refactor, or simplify
Comfortable using AI-assisted and agentic coding tools as a force multiplier—to accelerate development, improve code quality, and explore solutions faster, while maintaining strong engineering judgment and ownership of the final output.

Responsibilities

Design, build, and operate backend services and APIs that power Spring Labs’ core platform
Own data ingestion and processing pipelines (batch + real-time) for large, messy, multi-source datasets
Productionize machine-learning workflows by integrating models into backend systems with clear contracts, monitoring, and rollback paths
Design and maintain scalable service architectures (APIs, workers, queues, schedulers)
Improve system reliability through better testing, observability, error handling, and automation
Partner closely with data science to turn experiments into durable, production-grade systems
Influence architecture decisions and set backend engineering standards as the platform scales