At a Glance
- Tasks: Design and scale deployment layers for cutting-edge distributed technology platforms.
- Company: Venture-backed AI startup focused on optimising large-scale infrastructure.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and advanced automation.
- Why this job: Join a mission-driven team making global operations more resilient and sustainable.
- Qualifications: Strong Python skills and experience in distributed computing and resource management.
The predicted salary is between 59400 - 72600 £ per year.
London (1-2 days per week in office) AI Startup
About the Company
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 decentralised networks. Their mission is to make global industrial operations more resilient, secure, and sustainable through advanced automation.
About the Role
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.
Key Responsibilities
- Network Orchestration & System Performance
- 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.
- Scalable Infrastructure & Automation
- Simulation Frameworks: Develop and maintain large-scale virtualised environments to safely emulate real-world networks and system behaviours 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 optimisation, resource tuning, and hardware acceleration on varied architectures.
- Diagnostics & Engineering Standards
- 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.
Candidate Profile
- Core Technical Experience
- 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 optimising software performance.
- Production Operations: Solid track record establishing telemetry, tracking system health, and managing alerting rules in distributed or containerised ecosystems.
- Preferred Technical Exposure
- 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 specialised hardware acceleration toolchains.
- 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.
ML System Engineer - (Reinforcement Learning) employer: Roc Search
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StudySmarter Expert Advice🤫
We think this is how you could land ML System Engineer - (Reinforcement Learning)
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Roc Search or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Roc Search.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Roc Search.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Roc Search that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace ML System Engineer - (Reinforcement Learning)
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Roc Search.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Roc Search and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Roc Search
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Roc Search uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
