Senior Software Engineer

We're seeking a Senior Software Engineer who excels at building high-performance distributed systems and thrives in a fast-paced startup environment. You'll be working on cutting-edge data infrastructure challenges that directly impact how Fortune 500 industrial companies manage their most critical operational data.
Responsibilities:
Platform Ownership: Design, build, and operate the core serverless execution engine and workflow orchestration layer that serve as foundational primitives for CDF's AI and automation capabilities.
Reliability Engineering: Own uptime, latency SLOs, and incident response for platform services, ensuring functions execute deterministically and workflows progress without data loss or silent failures.
Scalability: Architect for multi-tenant and multi-cloud, high-throughput workloads. Design scheduling, queueing, and retry mechanisms that degrade gracefully under pressure.
API Design: Define and evolve clean API-first architecture, versioned REST, and event-driven APIs that downstream engineering teams and external customers depend on.
Observability: Instrument services with distributed tracing, structured logging, and alerting (OpenTelemetry / Prometheus / Grafana / Honeycomb stack) so failures surface before customers notice.
CI/CD and Testing: Champion test automation, unit, integration, and smoke testsand maintain deployment pipelines that ship to production with confidence.
Performance: Profile and resolve bottlenecks in execution throughput, cold-start latencies, and cross-service call chains driving a snappy platform experience for industrial workloads.
Cost Efficiency (Bonus): Model compute and storage costs for function execution; identify and implement optimizations that reduce cloud spend without sacrificing reliability.
Requirements:
6-8 Years of Engineering: Proven track record building and operating production backend services at scale.
Expertise: Deep mastery of JVM languages (Kotlin preferred, Java acceptable), Python (FastAPI), distributed systems patterns, and cloud-native service design (Kubernetes, Azure, GCP, AWS, Private cloud).
Workflow and Orchestration: Hands-on experience with workflow engines (Conductor, Apache Airflow, or equivalent) and event-driven architectures (Kafka, Pub/Sub).
Data and Storage: Comfortable working with relational databases (PostgreSQL) and non-relational databases, object storage (Data Lakes), and caching layers (Redis) in multi-tenant environments.
Observability Stack: Practical experience with OpenTelemetry, Prometheus, and Grafana for instrumentation and operational insight.
ML Platform Exposure: Experience supporting ML workloads and notebooks in production, whether through job scheduling, resource management, experiment tracking integration, or model serving infrastructure.
Contextualization Domain (Bonus): Familiarity with industrial knowledge graph construction, entity resolution, or NLP/CV pipelines as they relate to industrial asset data is a strong differentiator.
Full-Stack Awareness (Bonus): Familiarity with React or TypeScript is a plus for consuming and dogfooding your own platform's developer tooling.
The Platform Thinking Spirit: A passion for building composable, well-documented, and automated platform systems that empower other engineers, including ML engineers, to build faster.
Good to have:
ML Workload Support: Build and extend platform primitives for compute scheduling, environment management, and secrets handling that enable ML engineers to run model training, fine-tuning, and batch inference jobs reliably.
Contextualization Pipelines: Support the engineering infrastructure behind Cognite's contextualization capabilities (entity matching, asset hierarchy inference, and P& ID parsing) by ensuring the platform can orchestrate long-running, GPU-aware, and data-intensive ML workflows without manual intervention.
Vector and Embedding Infrastructure (Bonus): Familiarity with serving or storing vector embeddings to support semantic search and RAG-based contextualization use cases.
Model Lifecycle Awareness: Understand model versioning, A/B experiment tracking, and the boundary between platform concerns and ML framework concerns, so the platform stays lean while ML teams stay unblocked.

Experience
6-9 yrs