Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Engineer in Genomics

NLP PEOPLE
London
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Research Engineer, Deep Learning for Cancer Genomics

Principle Data Engineer

Data Engineer - GSK0JP00107167

Machine Learning Engineer, Enterprise Research London, UK

Data Scientist (ML/AI)

InstaDeep, founded in 2014, is a pioneering AI company at the forefront of innovation. With strategic offices in major cities worldwide, including London, Paris, Berlin, Tunis, Lagos, Cape Town, Boston, and San Francisco, InstaDeep collaborates with giants like Google DeepMind and prestigious educational institutions like MIT, Stanford, Oxford, UCL, and Imperial College London. We are a Google Cloud Partner and a select NVIDIA Elite Service Delivery Partner. We have been listed among notable players in AI, fast-growing companies, and Europe’s 1000 fastest-growing companies in 2022 by Statista and the Financial Times. Our recent acquisition by BioNTech has further solidified our commitment to leading the industry.

Join us to be a part of the AI revolution!

InstaDeep is currently looking for a new Machine Learning Engineer to join our expanding Genomics team, located in either London or Paris. Our team is primarily dedicated to applied research, with a strong focus on language models. Our goal is to push the boundaries of genomics research by delivering valuable insights and breakthroughs that were previously unattainable.

As a Machine Learning Engineer within the Genomics team, you will play a pivotal role in advancing our mission to accelerate genomics research. Specifically, you will focus on developing cutting-edge AI and deep learning solutions tailored for DNA analysis. Your responsibilities will encompass contributing to our in-house machine-learning codebases and libraries. Your core tasks will involve designing, developing, and optimizing deep learning models, especially language models, with a primary emphasis on enhancing accuracy, efficiency, and scalability on large sequence datasets.

You will be working on a daily basis with expert computational geneticists committed to helping you thoroughly understand the project requirements, and your mission will be to explore potential solutions and implement the necessary strategies to achieve improved and innovative computational performance. Throughout this process, your role will also include the development of effective, modular, and sustainable software solutions and daily interactions with our team of AI researchers.

RESPONSIBILITIES

  1. Contribute to Our In-House Machine Learning Libraries: Develop and actively contribute to our in-house Machine Learning libraries.
  2. Implementing Algorithms and Research Ideas for Genomics Applications: Apply algorithms and research concepts to language models and deep learning techniques for genomics applications.
  3. Promote Good Engineering Practices: Encourage and support the adoption of sound engineering practices when translating research into reusable and maintainable code.
  4. Design and Implement Algorithms for Modern Hardware: Create and deploy algorithms optimized for modern hardware and distributed computing systems, such as CPUs, GPUs, TPUs, and cloud infrastructure.
  5. Effective Reporting and Presentation: Clearly and efficiently communicate experimental results and research findings both internally and externally, both in written and verbal formats.
  6. Collaboration with Cross-Functional Teams: Collaborate closely with cross-functional teams, including computational geneticists and AI researchers, to seamlessly integrate AI solutions into genomics workflows.
  7. Stay Current with AI and Genomics Advancements: Keep abreast of the latest advancements in AI and genomics research. Contribute to scientific publications and explore innovative approaches to address genomics challenges.
  8. Develop Comprehensive Benchmarks: Create robust evaluation metrics and benchmarks for assessing AI model performance. Continuously refine and enhance models based on feedback.
  9. Thorough Documentation: Document your work comprehensively to ensure clear and reproducible results. Contribute to internal knowledge sharing for the benefit of the team.

QUALIFICATIONS

  1. A postgraduate degree in Computer Science, Machine Learning, or a related scientific field.
  2. Proven experience in deep learning, neural networks, and the development of AI models. Strong expertise in language models, particularly in transformers.
  3. Proficiency in programming languages such as Python, along with familiarity with libraries like TensorFlow, PyTorch, or Jax.
  4. While domain knowledge in genomics is not mandatory, a genuine curiosity about genomics data, tools, and databases is highly advantageous.
  5. Strong problem-solving skills and a creative mindset to address complex challenges in genomics research.
  6. Excellent communication skills to facilitate productive collaboration within multidisciplinary teams.
  7. A record of publications in the fields of AI, deep learning, or genomics research is considered a valuable bonus.

Our commitment to our people

We empower individuals to celebrate their uniqueness here at InstaDeep. Our team comes from all walks of life, and we’re proud to continue encouraging and supporting applicants from underrepresented groups across the globe. Our commitment to creating an authentic environment comes from our ability to learn and grow from our diversity, and how better to experience this than by joining our team? We operate on a hybrid work model with guidance to work at the office at least 2 to 3 days per week to encourage close collaboration and innovation. We are continuing to review the situation with the well-being of InstaDeepers at the forefront of our minds.

Right to work: Please note that you will require the legal right to work in the location you are applying for.

Company:

InstaDeep

Qualifications:Language requirements:Specific requirements:Educational level:Level of experience (years):

Senior (5+ years of experience)

Tagged as:Academia,Language Modeling,Machine Learning,Neural Networks,NLP,United Kingdom

#J-18808-Ljbffr

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.