Software Engineer (ML Infra)

Adamas Knight
London
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer - Data Engineering

Data Engineer - DV Cleared

Data Engineer - SC Cleared

Lead Data Engineer

AI Engineer / Machine Learning Engineer

Senior Data Engineer - Azure & Snowflake

About the job


Adamas Knight is recruiting for a groundbreaking AI Lab, backed by some of the biggest names in industry, working on building their own proprietary foundation model within the multi-modal domain - text and vision.


With one of the best compute in industry, they are looking for a ML Infrastructure Engineer to join the team.


The Role


As a ML Infrastructure Engineer, you will be instrumental in designing, building, and optimizing the infrastructure that supports their deep learning models. Working closely with the Research Scientist and Engineers, you will be central to creating robust machine learning pipelines, managing computational resources, and automating workflows, enabling our team to innovate and deploy AI models at scale.


You will:


  • Design and Optimize ML Pipelines: Build and maintain end-to-end machine learning pipelines, including data pre-processing, model training, evaluation, and deployment automation.
  • Infrastructure Management: Develop and manage scalable cloud-based and/or on-prem infrastructure to support the execution of machine learning experiments and model training (e.g., AWS, GCP, Azure, Kubernetes, Docker).
  • Model Deployment: Work closely with AI researchers to ensure seamless deployment of machine learning models into production environments, focusing on scalability, reliability, and performance.
  • Automate Workflow and Resource Management: Implement tools and automation scripts to optimize the use of computing resources, including the management of GPU/TPU resources and distributed training infrastructure.
  • Monitoring and Scaling: Build monitoring solutions to track performance, usage, and reliability of ML models and infrastructure, ensuring that systems scale rapidly as needed.
  • Continuous Improvement: Stay up to date with the latest trends and advancements in machine learning infrastructure and MLOps, and apply them to enhance team productivity and system performance.


Benefits/Perks


Attractive Compensation:Enjoy a competitive salary and the opportunity to invest in your future with equity in the company

Comprehensive Benefits:Access private healthcare, a gym allowance, and catered lunches to support your well-being

Work-Life Balance:Benefit from flexible working hours that fit your lifestyle



At Adamas Knight, we are committed to creating an inclusive culture. We do not discriminate based on race, religion, gender, national origin, sexual orientation, age, veteran status, disability, or any other legally protected status. Diversity is highly valued, and we encourage applicants from all backgrounds to apply.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.