Software Engineer (ML Infra)

Adamas Knight
London
11 months ago
Applications closed

Related Jobs

View all jobs

Software Engineer - AI MLOps Oxford, England, United Kingdom

Software Engineer III- Data Engineer, Java/Python

GenAI Software Engineer/Data Scientist

Lead Software Engineer - Agentic AI/Machine Learning

AI/ML Software Engineer III — GenAI & NLP Pipelines

Software and Data Engineer

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.