ML Systems Engineer; RL

Our client is a venture-backed technology company building software to optimise large-scale physical infrastructure. Their platform processes data locally at the source using decentralized networks. Their mission is to make global industrial operations more resilient, secure, and sustainable through advanced automation.

Our client is seeking an Infrastructure Engineer to design and scale the deployment layer of their distributed technology platform. The core challenge involves orchestrating complex, concurrent software applications and analytical workloads across a massive network of diverse, on-premise hardware installations.

In this position, you will own the systems engineering required to guarantee that these disparate applications execute reliably within strict memory and compute constraints. You will also collaborate directly with their research and engineering teams, building robust testing environments, scaling distributed pipelines, and converting experimental concepts into dependable production software.

• Resource Management: Design runtime isolation, task scheduling, and resource allocations for multiple concurrent local processes sharing the same hardware.
• System Synchronization: Build robust reconciliation mechanisms to ensure atomic updates and version alignment across remote environments.
• Release Management: Architect deployment flows supporting progressive rollout strategies, passive validation modes, safety guardrails, and automated recovery loops.

• Simulation Frameworks: Develop and maintain large-scale virtualized environments to safely emulate real-world networks and system behaviors for validation.
• Pipeline Automation: Construct fault-tolerant distributed processing networks that support automated state saving, failure recovery, and cross-site data flows.
• Performance Optimization: Profile system execution to improve processing performance through code optimization, resource tuning, and hardware acceleration on varied architectures.

• Data Streams: Establish reliable telemetry and ingestion channels that preserve data lineage for downstream analytic workflows.
• System Telemetry: Implement comprehensive dashboard metrics, unified logging, and warning systems to detect system degradation or variance early.
• Engineering Rigour: Troubleshoot deep architectural bugs, assist engineering teams with technical blockers, and enforce high coding standards via thorough review processes.

• Software Foundations: Exceptional software engineering capabilities in production-level Python, with a strong focus on clean testing patterns and modular design.
• Distributed Computing: Extensive experience managing state alignment, messaging, and system execution across inconsistent networks and varied hardware form factors.
• Resource Partitioning: Demonstrated skill in managing system memory, computing bounds, and storage across competing local application tasks.
• Systems Infrastructure: Deep operational familiarity with managing background workloads, handling checkpointing/recovery, and optimizing software performance.
• Production Operations: Solid track record establishing telemetry, tracking system health, and managing alerting rules in distributed or containerized ecosystems.

• Experience building infrastructure for simulation software, virtual testbeds, or automated control systems.
• Experience with Reinforcement Learning tools such as Ray RLlib
• Familiarity with high-efficiency runtime environments or specialized hardware acceleration tool chains.
• Background in remote system provisioning, telemetry transport protocols (such as messaging queues), or remote software updates.
• Experience maintaining custom hardware environments, private network setups, or software for highly regulated environments.