Distributed ML Systems Engineer - Hybrid (London)

Roc Search Inc.; Greater London; Hybrid; GBP 100,000 - 110,000; Full time

Job summary

AI Startup in London is seeking a Machine Learning Systems Engineer to design and scale the deployment layer of a distributed platform. You will orchestrate concurrent apps across a vast on-premise network, ensuring reliable execution within memory and compute constraints.

You will collaborate with research and engineering teams, build testing environments, and scale distributed pipelines while turning experimental concepts into production software. Hybrid in-office pattern with weekly schedule.

Qualifications

• Proficient production-grade Python development with strong testing patterns.
• Experience delivering distributed systems and concurrent workloads.
• Familiarity with monitoring and observability tooling in distributed apps.
• Knowledge of memory and compute constraints in large-scale deployments.
• Experience with ML platforms and model deployment workflows.

Responsibilities

• Resource Management: design runtime isolation, scheduling, and allocations for multiple local processes.
• System Synchronization: build reconciliation to ensure atomic updates and version alignment across environments.
• Release Management: architect deployment flows with progressive rollout, validation modes, guardrails, and recovery loops.
• Simulation and testing: create robust testing environments for distributed workloads.
• Pipeline Automation: build fault-tolerant networks with state saving and cross-site data flows.
• Performance optimization: profile and tune code and hardware acceleration.
• Telemetry: establish data streams and dashboards for system health.
• Engineering Rigour: assist teams with blockers and enforce coding standards.
• Documentation and knowledge sharing for robust operations.

Skills

Python

MLops

Pytorch

Grafana

Elastic Search

TensorRT

Job description

AI Startup in London is seeking a Machine Learning Systems Engineer to design and scale the deployment layer of a distributed platform. You will orchestrate concurrent apps across a vast on-premise network, ensuring reliable execution within memory and compute constraints.

You will collaborate with research and engineering teams, build testing environments, and scale distributed pipelines while turning experimental concepts into production software. Hybrid in-office pattern with weekly schedule.