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.
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.
- 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.
- 10+ 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.
- 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.
Skills Required
- 10+ years of engineering experience building and operating production backend services at scale
- Deep expertise in JVM languages, preferably Kotlin; Java is acceptable
- Python and FastAPI experience
- Experience with distributed systems patterns and cloud-native service design
- Experience with Kubernetes and at least one of Azure, Google Cloud Platform, Amazon Web Services, or private cloud environments
- Hands-on experience with workflow engines such as Conductor, Apache Airflow, or equivalent
- Experience with event-driven architectures using Kafka, Pub/Sub, or equivalent
- Experience with relational and non-relational databases, object storage, data lakes, and Redis caching in multi-tenant environments
- Practical experience with OpenTelemetry, Prometheus, and Grafana
- Experience supporting machine learning workloads, notebooks, job scheduling, resource management, experiment tracking, or model-serving infrastructure
- Familiarity with industrial knowledge graphs, entity resolution, or NLP/CV pipelines
- Familiarity with React or TypeScript
- Familiarity with vector embeddings, semantic search, or retrieval-augmented generation infrastructure
- Understanding of model versioning, A/B experiment tracking, and ML platform lifecycle concerns
Cognite Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cognite and has not been reviewed or approved by Cognite.
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Affordable Benefits — Healthcare premiums for employees and dependents are often fully covered, reducing out‑of‑pocket costs. Feedback suggests this makes the total rewards feel competitive in key markets.
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Parental & Family Support — Paid parental leave for primary and secondary caregivers is described as generous. This signals a family‑friendly approach that many consider a standout perk.
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Leave & Time Off Breadth — Unlimited PTO with a company‑wide year‑end shutdown offers ample time away from work. Flexible time‑off options are consistently highlighted across U.S. roles.
Cognite Insights
What We Do
Cognite is an AI company that delivers industrial software to improve the production efficiency of Energy, Process Manufacturing, and other industrial companies. We deliver an Industrial DataOps platform that liberates siloed data and empowers our customers to solve some of their most complex business problems with AI-powered solutions. The typical solutions we enable drive innovative new ways to approach Data Exploration, Digital Operator Rounds, Production Optimization, Turnaround Planning, and Root Cause Analysis. We do this by automating and scaling industrial data contextualization of various sources (such as time series, engineering diagrams, equipment logs, maintenance records, 3D facility models, images, large point clouds, and more). We use AI and other tools to find and map the meaningful relationships between the data across these various sources. In addition, we provide intuitive tools that enable efficient use of analytics and automated workflows, as well as prebuilt AI capabilities and a low-code industrial agent builder, Cognite Atlas AI, that enables AI to carry out more complex operations with greater accuracy.
Why Work With Us
Employees at Cognite are pushing the envelope with the latest cloud technology, scaling industrial applications across hundreds of assets, revolutionizing industrial data models, and working with robotics. Cogniters are fast, creative, and resilient. We keep the energy high and fun, learning from our mistakes and celebrating our victories together.
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