Machine Learning Engineer in Genomics

Key Skills

Aartificial intelligence, Algorithms, Deep Learning, Effective communication skills, Genomic data, GPUs, JAX framework, Machine learning techniques, PyTorch, TensorFlow

Job Description

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

• Contribute to Our In-House Machine Learning Libraries: Develop and actively contribute to our in-house Machine Learning libraries.
• Implementing Algorithms and Research Ideas for Genomics Applications: Apply algorithms and research concepts to language models and deep learning techniques for genomics applications.
• Promote Good Engineering Practices: Encourage and support the adoption of sound engineering practices when translating research into reusable and maintainable code.
• 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.
• Effective Reporting and Presentation: Clearly and efficiently communicate experimental results and research findings both internally and externally, both in written and verbal formats.
• 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.
• 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.
• Develop Comprehensive Benchmarks: Create robust evaluation metrics and benchmarks for assessing AI model performance. Continuously refine and enhance models based on feedback.
• Thorough Documentation: Document your work comprehensively to ensure clear and reproducible results. Contribute to internal knowledge sharing for the benefit of the team.

QUALIFICATIONS

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