Senior Data & MLOps Engineer - GPU Reliability Platform

Coreweaveu; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Pension Contribution

Life Insurance

Critical Illness Cover

Employee Assistance

Tuition Reimbursement

Disruption-focused culture

Job summary

CoreWeave is seeking a Senior Data & MLOps Engineer to design and scale the GPU Intelligence Platform infrastructure. You will build data pipelines, feature processing, and production-grade ML model deployment across a fleet, with a focus on scalability, real-time and offline processing, and resource management.

You will work with Kubernetes-based microservices, collaborate with Platform teams, and implement monitoring and diagnostic signals to continuously improve system performance and

Qualifications

• 7+ years of experience in data engineering, distributed systems, MLOps, or infrastructure ML roles in production environments.
• Proven experience building high-throughput streaming or telemetry pipelines (e.g., Kafka, Pulsar, Kinesis, or equivalent).
• Strong experience designing time-series feature pipelines and operating large-scale observability systems.
• Experience building and maintaining feature stores and ensuring offline/online feature parity.
• Hands-on experience deploying ML models to production, including versioning, monitoring, rollback, and drift detection.
• Experience designing scalable microservices deployed in Kubernetes-based environments.
• Strong proficiency in Python and at least one systems language (Go, Rust, or C++).
• Experience working with distributed compute or training systems (e.g., NCCL, PyTorch Distributed, Spark, Ray, Slurm).
• Familiarity with GPU telemetry systems such as NVML or DCGM and hardware-level monitoring concepts.
• Demonstrated experience scaling systems from Proof-of-Concept to production-grade, fleet-level deployments.

Responsibilities

• Design and implement scalable data ingestion pipelines.
• Build feature processing and baseline computation systems.
• Productionize models for prediction and detection.
• Develop and operate low-latency service and robust offline workflows.
• Architect horizontally scalable services with clear separation between components, leveraging orchestration for distribution.
• Implement monitoring and feedback loops for continuous model and signal improvement.
• Collaborate with Platform teams to integrate operational signals into monitoring and diagnostics.
• Implement a scalable solution for mitigation and structured analysis.

Skills

Data engineering

Distributed systems

MLOps

Kubernetes

Python

Go/Rust/C++

Streaming pipelines

Feature stores

Model deployment

Observability

Tools

Kafka

Pulsar

Kinesis

NCCL

PyTorch

Spark

Ray

Slurm

NVML

Job description

CoreWeave is seeking a Senior Data & MLOps Engineer to design and scale the GPU Intelligence Platform infrastructure. You will build data pipelines, feature processing, and production-grade ML model deployment across a fleet, with a focus on scalability, real-time and offline processing, and resource management.

You will work with Kubernetes-based microservices, collaborate with Platform teams, and implement monitoring and diagnostic signals to continuously improve system performance and