Senior Backend Engineer

At HumanSignal, we’re building the platform that powers the creation, curation, and evaluation of that data. From fine-tuning foundation models to validating agent behaviors in production, our tools are used by leading AI teams to ensure models are grounded in real-world signal, not noise.

Our open-source product, Label Studio, has become the de facto standard for labeling and evaluating data across modalities — from text and images to time series and agents-in-environments. With over 250,000 users and hundreds of millions of labeled samples, it’s the most widely adopted OSS solution for teams working on building AI systems. 

Label Studio Enterprise builds on that traction with the security, collaboration, and scalability features needed to support mission-critical AI pipelines — powering everything from model training datasets to eval test sets to continuous feedback loops.We started before foundation models were mainstream, and we’re doubling down now that AI is eating the world. If you're excited to help leading AI teams build smarter, more accurate systems — we’d love to talk.

This is a product-engineering role: not “build what you’re told,” but shape what we build—own the problem, propose solutions, and ship outcomes that matter to users and the business.

We’re hiring a Senior Backend Engineer with product vision. You’ll design and operate services in Python/Django that power labeling and evaluation workflows at scale: clean API contracts, robust data models, efficient background jobs, and dependable performance. You’ll partner early with PM/Design/FE to frame problems, prototype to learn, and make pragmatic scope calls—so complex workflows feel simple, reliable, and fast for users.

• →Design, build, and operate backend services/APIs in Python/Django with Postgres/Redis.
• →Own outcomes, not tasks: refine scope, suggest trade-offs, and land increments that deliver value quickly and safely.
• →Model data and author migrations; tune queries and caching; manage background jobs/queues for high-volume workflows.
• →Evolve API contracts with FE; keep interfaces clear, versioned, and resilient to change.
• →Improve reliability and performance (timeouts, retries, idempotency, rate limits, pagination, backpressure).
• →Implement secure, multi-tenant patterns: auth, permissions/RBAC, auditability, and safe data boundaries.
• →Contribute to testing strategy (unit/integration), error handling, and graceful failure modes.
• →Partner with Support to investigate production issues and turn learnings into fixes and prevention.
• →Engage with our open-source community (issues, discussions) to understand real-world needs and improve developer experience.
• →Participate in architecture discussions and code reviews; mentor teammates and improve patterns across the codebase.
  
  

• Senior-level experience shipping production backends in Python/Django (or a close equivalent) and SQL.
• Strong product sense: you’ve made scope/trade-off decisions and iterated based on real-world usage and feedback.
• Depth in REST API design, relational modeling, migrations, and performance tuning.
• Experience with background processing/queues (e.g., Celery/RQ/Kafka-backed workers) and operational concerns.
• Clear written communication at a Senior IC level—concise RFCs/PRDs, actionable reviews, and crisp decision records.
• Nice to have: GraphQL familiarity; Kafka/streaming; Spark/BigQuery; security/compliance exposure; multi-region architectures.
  
  

Python/Django, JS/TS, React, OpenAI API, Spark, BigQuery, Kafka, Jest, Cypress, AWS, Kubernetes, Postgres, Redis.

Trunk-based development with small, reviewable PRs; feature flags for safe rollouts; strong code reviews; pragmatic testing; and close collaboration with Product/Design/Support.

At HumanSignal, we follow a six-week cycle known as "Build and Ship" followed by a "Cooldown." During the first four weeks, the team focuses on creating new features and shipping improvements. We also rotate a small group to handle customer support, ensuring everyone stays in touch with real user needs and we can respond quickly to issues.

After those four weeks, we have a two-week "Cooldown" period. This is when we tackle technical debt, refine our integration processes, and wrap up those lingering tasks that never quite fit into regular sprints but really help us feel good about our codebase and workflows.