Machine Learning Engineer | Omics | RNA | DNA | PyTorch | Hybrid, London

Enigma; Greater London; Hybrid; GBP 50,000 - 90,000; Full time

Job summary

An innovative firm is seeking a Machine Learning Engineer to contribute to groundbreaking genomic foundation models. This role involves training complex models and curating genomic data to support the development of state-of-the-art -omics technologies. The ideal candidate will possess a strong background in machine learning, particularly within biological contexts, and be eager to collaborate with a talented team. This position offers the chance to work at the forefront of biotechnology research, leveraging cutting-edge tools and techniques to drive advancements in gene editing and drug discovery. Join a dynamic team that values creativity and scientific rigor!

Qualifications

• Postgraduate experience in ML with application to biological domain.
• Experience building modern ML architectures and applying to datasets.

Responsibilities

• Develop proprietary -omics models, including model training and evaluation.
• Collaborate with bioinformatics team for data ingestion and QC.

Skills

Machine Learning

Genomic Data Analysis

Model Training

Data Curation

Deep Learning

Transformers

Multi-modality

Education

Postgraduate experience (MSc or PhD) in ML

Tools

PyTorch

Huggingface Transformers

ML Accelerators

Sagemaker

Vertex AI

Job description

Machine Learning Engineer | Omics | RNA | DNA | PyTorch | Hybrid, London

While gene-editing is becoming increasingly efficient, identifying which genes to edit and how remains a significant challenge. To overcome this bottleneck, we use cutting-edge deep learning to accurately and efficiently identify high-value genetic targets for gene-editing.

Our approach draws inspiration from recent advancements in the drug discovery space, incorporating large language models (LLMs), transformers, and graph-based technologies to build a best-in-class discovery platform for plant sciences.

Team

Our team is currently composed of 12 members, including ML engineers, data engineers, and bioinformaticians. We also have a remote, part-time intern conducting ML research. The team primarily works together in person at our office in London 4 days per week.

Position

As part of the core ML team, you will help us build genomic foundation models. Your responsibilities could range from model training to data curation to evaluations. We welcome applicants with specific expertise who feel they could uniquely contribute to the training lifecycle of large, complex models.

The ideal applicant will have experience using genomic data in a machine learning context. We are particularly interested in individuals with experience working with foundational generative models of DNA or transcriptomic data. However, our modelling efforts have a strong focus on multi-modality, so experience with or interest in other data modalities (e.g., text) is a plus.

Core Responsibilities

• Contribution to the development of proprietary -omics models, including model training and evaluation development.
• Recreation of state-of-the-art models from the scientific literature and benchmarking against internal models and evaluations.

Additional / Development Areas

• Model deployment to ensure flexible and scalable inference access to the wider Data Science team.
• Collaboration with the bioinformatics team to ingest, standardize, and QC data from multiple sources (internal and external) for use in training pipelines.
• Support for the wider ML team on model development and commercial projects.

Core

• Postgraduate experience (MSc or PhD) in ML with a demonstrated application to a biological domain.
• Experience building modern ML architectures (e.g., transformers, diffusers) from scratch and applying them to real biological datasets.
• Experience working with large-scale transcriptomic datasets, ideally from non-human organisms (though not required).
• Experience with PyTorch, huggingface transformers, and diffusers.
• Experience working with ML accelerators.

Nice-to-have

• Relevant publications in reputable journals or contributions to open-source projects.
• Exposure to and interest in probabilistic ML, causal ML, or active learning.
• Experience with distributed model training (data and model parallelism).
• Experience working on biological data curation, including data cleansing and preprocessing of -omics datasets.
• Exposure to cloud-based ML orchestration frameworks such as Sagemaker and Vertex AI.
• Experience with model deployment in an enterprise setting.

For immediate consideration please send your most up to date CV to [e-mail]

Seniority level: Mid-Senior level

Employment type: Full-time

Job function: Information Technology

Industries: Staffing and Recruiting and Biotechnology Research