Machine Learning Engineer in Genomics

NLP PEOPLE
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer III

Machine Learning Engineer II

Machine Learning Engineer II

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.