Staff Security Engineer Data Science Engineering
Nscale is a London-based, full-stack AI cloud and infrastructure company that provides GPU compute, managed AI services, orchestration software, data centers, and power infrastructure for AI training, fine-tuning, and inference.
Funding history
About Nscale
Nscale builds and operates vertically integrated AI infrastructure spanning software, GPU compute, networking, storage, purpose-built data centers, and power. Its active cloud platform offers self-service inference endpoints, fine-tuning, managed Kubernetes and Slurm, virtual machines, and GPU clusters.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and operate security-data pipelines across endpoint, identity, cloud, network, vulnerability, SaaS, and internal-platform telemetry. You will measure data quality, detect collection gaps, create behavioral and cross-source detections, support incident investigations, and prepare governed datasets for AI-assisted analysis.
Requirements
- 8+ years of experience in data engineering, data science, detection engineering, security analytics, or a related technical field, including significant security-data work.
- Experience building and operating security data pipelines, including API and event-source collection, idempotent loads, partitioning, backfill, replay, and schema evolution.
- Experience with a cloud warehouse and streaming system, such as BigQuery and Kafka.
- Knowledge of security telemetry across identity, endpoint, cloud, network, vulnerability, and SaaS audit logs.
- Ability to apply statistical or machine-learning methods to security analysis and translate results into usable detections.
- Ability to communicate coverage gaps and uncertain findings clearly.
Responsibilities
- Own the inventory of security-relevant data sources and document ownership, collection, freshness, coverage, and gaps.
- Build and operate idempotent batch and streaming collection pipelines from APIs, event streams, object stores, and internal systems.
- Monitor data freshness, volume, completeness, duplicates, schema drift, and cross-source consistency.
- Make pipeline failures visible and support backfill and replay.
- Build and evaluate behavioral baselines, anomaly detection, and cross-source correlations.
- Support major incidents with scoping, timelines, and pattern analysis.
- Prepare curated and documented datasets for AI-assisted analysis with audited access and provenance.
- Coordinate access, schemas, and collection cadences with source-system owners.
Benefits
- Bonus
- Equity
- Flexible workplace
- Medical coverage
- Dental coverage
- Vision coverage
- Flexible paid time off
- Parental leave
- Retirement plan participation