Senior Software Engineer - Platform

Cognite - AI for Industry; United States; On-site; USD 120,000 - 190,000; Full time

Job summary

Cognite is seeking a Software Engineer to build high-performance distributed data infrastructure in a fast-paced startup environment. You will own core serverless execution and workflow orchestration, ensure uptime and latency targets, and design multi-tenant, multi-cloud systems that scale for Fortune 500 industrial partners.

You will work with Kotlin/Java, Python, Kubernetes, and various data technologies, embracing observability and CI/CD practices to deliver reliable platform services across

Qualifications

• 6–8 years of engineering experience building and operating production backend services at scale.
• Expertise in JVM languages (Kotlin preferred, Java acceptable) and Python (FastAPI).
• Strong understanding of distributed systems patterns and cloud-native service design (Kubernetes, Azure, GCP, AWS, private cloud).
• Hands-on experience with workflow engines (Conductor, Apache Airflow) and event-driven architectures (Kafka, Pub/Sub).
• Experience with relational (PostgreSQL) and non-relational databases, object storage, and caching (Redis).
• OpenTelemetry, Prometheus, Grafana experience for observability.

Responsibilities

• Design, build, and operate the core serverless execution engine and Workflows orchestration layer.
• Own uptime, latency SLOs, and incident response for platform services.
• Architect multi-tenant and multi-cloud scaled systems with efficient scheduling and retry mechanisms.
• Define and evolve REST and event-driven APIs for internal and external consumers.
• Instrument services with tracing, logging and alerting to surface failures early.
• Champion test automation and CI/CD pipelines for production deployment.

Skills

Kotlin

Java

Python

Distributed systems

Kubernetes

Azure

GCP

AWS

Conductor

Airflow

Kafka

Pub/Sub

Redis

Tools

Conductor

Apache Airflow

Kafka

Pub/Sub

Job description

What Cognite is: Relentless to achieve

Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.

About The Role

We're seeking a 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.

How you’ll demonstrate Ownership

• Platform Ownership: Design, build, and operate the core serverless execution engine and Workflows 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 & 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 (Open-telemetry / Prometheus / Grafana / Honeycomb stack) so failures surface before customers notice.
• CI/CD & Testing: Champion test automation - unit, integration, and smoke tests and 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 functions execution; identify and implement optimizations that reduce cloud spend without sacrificing reliability.

The Impact you bring to Cognite

• 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 & Orchestration: Hands-on experience with workflow engines (Conductor, Apache Airflow, or equivalent) and event-driven architectures (Kafka, Pub/Sub).
• Data & Storage: Comfortable working with relational databases (PostgreSQL) & non-relational databases, object storage(Data-lakes), and caching layers (Redis) in multi-tenant environments.
• Observability Stack: Practical experience with Open-telemetry, Prometheus, and Grafana for instrumentation and operational insight.
• ML Platform Exposure: experience supporting ML workloads & notebooks in production, whether through job scheduling, resource management, experiment tracking integration, or model serving infrastructure.
• Contextualisation 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 Platform & Contextualisation:

• ML Workload Support: Build and extend platform primitives compute scheduling, environment management, and secrets handling, that enable ML engineers to run model training, fine-tuning, and batch inference jobs reliably.
• Contextualisation Pipelines: Support the engineering infrastructure behind Cognite’s Contextualisation capabilities (entity matching, asset hierarchy inference, P&ID parsing) by ensuring the platform can orchestrate long-running, GPU-aware, and data-intensive ML workflows without manual intervention.
• Vector & Embedding Infrastructure (Bonus): Familiarity with serving or storing vector embeddings to support semantic search and RAG-based contextualisation 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.

Learn more about us

• Impact 2025
• Cognite's Industrial AI: Moonshot
• We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.

Equal Opportunity

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.