What You'll Do

● Design, build, and maintain backend services that power Integral's data operations platform

● Rapidly prototype and productionize support for new data modalities, moving from ambiguous customer requirements or emerging research to pragmatic backend solutions in tight timelines

● Support deadline-driven customer work from time to time, balancing speed, quality, and long-term platform durability

● Develop systems that can adapt to evolving AI/ML use cases across healthcare, enterprise, and other regulated or sensitive data environments

● Develop scalable APIs, microservices, and internal tools that support core product workflows

● Build and optimize data ingestion, transformation, validation, and delivery pipelines for high-volume healthcare data

● Work across databases, queues, object storage, and distributed processing systems to support reliable data movement at scale

● Improve system performance, reliability, observability, and fault tolerance across backend services

● Design backend architecture that is secure, maintainable, and able to scale with customer and product growth

● Partner with Product and Solutions Engineering to translate complex customer and data infrastructure needs into durable platform capabilities

● Build backend systems that support AI-enabled workflows, including data preparation, automation, retrieval, evaluation, and model-adjacent infrastructure

● Optimize database schemas, queries, indexing strategies, and storage patterns for performance and maintainability

● Develop cloud-native infrastructure using tools and platforms such as AWS, Docker, Kubernetes, Terraform, and CI/CD systems

● Create clean abstractions, reusable services, and internal frameworks that help the engineering team move faster

● Debug complex production issues across services, infrastructure, data pipelines, and customer environments

● Contribute to technical design discussions, architecture reviews, and engineering best practices

● Operate with high ownership in a fast-moving startup environment where priorities evolve quickly and ambiguity is the norm

What We're Looking For

● 3-6+ years of experience as a backend software engineer, infrastructure engineer, data platform engineer, or similar technical role

● Strong backend engineering experience building production-grade services, APIs, and distributed systems

● Experience designing and operating scalable data pipelines, ETL/ELT workflows, or data-intensive backend systems

● Proficiency in one or more backend programming languages such as Python, Go, Rust, , , TypeScript, or similar

● Strong understanding of databases, data modeling, query optimization, indexing, and transactional systems

● Experience with cloud platforms such as AWS, GCP, or Azure

● Experience with containerization, orchestration, and infrastructure tooling such as Docker, Kubernetes, Terraform, or similar

● Familiarity with event-driven systems, queues, background jobs, workflow orchestration, or distributed processing patterns

● Ability to write clean, maintainable, well-tested code and make thoughtful engineering tradeoffs

● Comfort working across the full backend stack, from API design and business logic to infrastructure, observability, and production debugging

● Strong systems thinking and an ability to reason about scalability, reliability, latency, security, and data integrity

● Interest in AI-enabled products, data infrastructure, or building backend systems that support AI/ML workflows

● Energized by fast-paced startup environments—you're excited to build, iterate, and wear multiple hats as the company scales

● Strong communication skills and ability to collaborate effectively with product, engineering, and customer-facing teams

● An ownership mentality—you proactively identify gaps, improve systems, and drive projects from idea through production

Nice to Have

● Experience building data platforms, workflow engines, developer platforms, or infrastructure for data-heavy products

● Experience with AI/ML infrastructure, LLM applications, retrieval systems, model-serving workflows, evaluation pipelines, or data labeling systems

● Experience working with healthcare data, regulated data, privacy-preserving systems, or enterprise compliance requirements

● Experience with Postgres, Redis, Kafka, Airflow, Celery, Spark, or similar data/backend infrastructure tools

● Experience building systems that process large volumes of structured and unstructured data

● Experience with observability tools, distributed tracing, metrics, logging, and incident response

● Experience working in early-stage or high-growth startup environments

● Familiarity with security best practices for data-intensive systems, including access controls, auditability, encryption, and secure deployment patterns