National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

ML Engineer

Dalton
London
4 months ago
Applications closed

Related Jobs

View all jobs

Principal Engineer

Data Engineer - Data Infrastructure

Senior Software Engineer – API & ML Infrastructure

Machine Learning Engineer Trainer

Machine Learning Engineer Trainer

Machine Learning Engineer - Fixed Term Contract

About Dalton:Dalton is on a mission to make the world’s drug design more efficient. We are building the AI ecosystem for drug design and solving real-world problems that transform the efficiency of the pharmaceutical industry. Our mission is to harness cutting-edge technology and turn it into impactful products for our clients. Join us on our journey to revolutionize drug discovery and make a difference in the lives of patients worldwide. 

Why Join Dalton?Dalton offers an exciting and collaborative environment where you can contribute to improving the efficiency of the world’s drug discovery. We value innovation, creativity, and commitment. Join us in our mission to change the world. 

Role Overview:We are seeking a highly skilled and motivated ML Engineer to join our team dedicated to advancing drug discovery by translating the best AI Research intro drug discovery impact. The ideal candidate will have a strong background in ML engineering and a passion for leveraging data and AI to drive innovation in the pharmaceutical and biotechnology industries. 

If you are a dedicated and detail-oriented ML Engineer with a passion for drug discovery, we would love to hear from you. Apply now to become a part of our dynamic team and contribute to groundbreaking advancements in the field of technology and drug discovery. 

Requirements

Key Responsibilities: 

  • Collaborate with cross-functional teams to integrate machine learning models into impactful scientific products 
  • Analyze large-scale biological and chemical datasets to identify patterns and generate actionable insights 
  • Build a highly scalable machine learning platform that can be used to integrate cutting-edge ML models into a drug discovery process. 
  • Design, build and maintain scalable machine learning infrastructure to support high-throughput data processing. 
  • Continuously evaluate and improve model performance, ensuring high accuracy and reliability. 
  • Stay current with the latest advancements in machine learning for drug discovery and apply this knowledge to your work. 

Capabilities: 

  • Master's or Ph.D. in Computer Science, Bioinformatics, Computational Biology, Cheminformatics or a related field. 
  • Proven experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. 
  • Experience in working in the Pharmaceutical, Biotech or similar scientific discovery industries 
  • Strong programming skills in Python 
  • Familiarity with big data technologies such as Spark, Kafka, and Iceberg. 
  • Knowledge of data modeling, database design, and data warehousing concepts. 
  • Proficiency in data visualization tools like Superset, Grafana, or Metabase. 
  • Strong problem-solving skills and ability to work independently and as part of a team. 
  • Excellent organizational and time-management skills. 
  • Passion for drug discovery and a desire to make a significant impact in the field. 
National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.