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ML Platform Engineer
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a ML Platform Engineer based in United Kingdom.
This role offers the opportunity to build and evolve the infrastructure powering advanced AI products used at enterprise scale.
You will design reliable, scalable systems that enable machine learning teams to train, deploy, and operate complex models efficiently. Working at the intersection of software engineering, cloud infrastructure, and machine learning, you will help shape the future of AI platform capabilities. The position focuses on automation, reliability, performance optimization, and creating tools that improve how teams build and operate ML systems. You will collaborate closely with researchers and product engineers to transform technical challenges into robust platform solutions.
This is an ideal opportunity for a systems-focused engineer who enjoys solving complex infrastructure problems and driving meaningful improvements in AI development workflows.
Accountabilities:
- Design, develop, and improve platform systems supporting machine learning model training, evaluation, deployment, and production serving.
- Build scalable infrastructure and internal tooling that improve the reliability, efficiency, and cost-effectiveness of machine learning workloads.
- Develop automation workflows, internal tools, and agent-oriented systems that reduce operational complexity for researchers and engineers.
- Architect and maintain systems that enable efficient model deployment, monitoring, and operation across research and product environments.
- Improve workload scheduling, monitoring, debugging, and resource management for GPU-based and cloud infrastructure environments.
- Drive improvements across observability, automation, reliability, developer experience, and platform usability.
- Create abstractions and developer tools that enable engineering teams to work more effectively with complex ML systems.
- Collaborate with research and product teams to identify technical challenges and turn them into scalable platform capabilities.
- Contribute to architectural decisions, technical strategy, and long-term platform evolution.
- Take ownership of open-ended engineering challenges while making pragmatic decisions that balance scalability, simplicity, and reliability.
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Requirements:
- Strong professional experience building or operating production systems with a focus on reliability, scalability, performance, and maintainability.
- Strong systems mindset with the ability to reason about bottlenecks, failure scenarios, interfaces, resource utilization, and long-term operational needs.
- Hands-on experience with cloud infrastructure, Linux environments, and infrastructure automation.
- Experience operating distributed systems and workloads in production, including Kubernetes-based environments.
- Strong programming skills in Python or similar backend-oriented programming languages.
- Experience building internal platforms, developer tooling, infrastructure abstractions, or systems used by engineering teams.
- Understanding of machine learning infrastructure, model serving systems, or data-intensive workloads.
- Experience working with GPU-based systems, performance-sensitive environments, or large-scale computing resources.
- Familiarity with observability, monitoring, and debugging practices for distributed systems.
- Knowledge of infrastructure and development tools such as Terraform, Datadog, GitHub Actions, or similar technologies.
- Ability to work effectively in ambiguous environments, take ownership, and solve complex technical problems independently.
- Pragmatic approach to engineering, focusing on delivering valuable solutions without unnecessary complexity.
Preferred Skills & Experience:
- Experience building agentic systems or internal tools powered by large language models.
- Familiarity with workflow orchestration platforms such as Temporal.
- Experience working between research and production engineering teams.
- Background in performance optimization, scheduling, or resource allocation challenges.
- Experience developing lightweight tools or products for engineers and technical users.
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Benefits:
- Fully remote work environment with flexibility across Europe.
- Opportunity to work on advanced AI infrastructure supporting large-scale enterprise applications.
- High ownership role with significant influence over platform architecture and technical direction.
- Collaborative environment with close interaction between engineering, research, and product teams.
- Opportunity to solve complex challenges involving machine learning systems, automation, and distributed infrastructure.
- Professional growth opportunities within a fast-moving AI-focused organization.
- Ability to contribute to tools and systems that improve productivity for technical teams worldwide.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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