ML engineer in BIOTECH & genomics

Contract Role: Machine Learning Engineer – Biotech & Genomics Location: Hybrid (London based)Contract Type: 12–18 months, Outside IR35 Start Date: Immediate Rate: Competitive, based on experienceWe’re working with a group of biotech innovators focused on solving some of the most complex challenges in life sciences — from protein folding and genomic analysis to biomedical imaging and drug discovery. These teams are seeking a Machine Learning Engineer with deep technical expertise and a passion for applying AI to real-world biological problems.?? What You’ll Be Working On:

• Designing and deploying ML models for protein structure prediction, genomic pattern recognition, and cell image segmentation
• Collaborating with bioinformaticians, data scientists, and software engineers to build scalable pipelines for research and clinical applications
• Fine-tuning and optimizing models using tools like AlphaFold, RoseTTAFold, BioBERT, and DeepCell
• Integrating ML workflows with platforms such as Benchling, PubMed APIs, and internal R&D systems
• Supporting model validation, performance benchmarking, and regulatory documentation

 Key Technologies & Tools:

• ML Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
• Bio-AI Tools: AlphaFold, RoseTTAFold, BioBERT, DeepCell, Cellpose
• Data Sources: Genomic datasets, microscopy images, biomedical literature
• Cloud & DevOps: AWS/GCP, Docker, Kubernetes, MLflow
• Languages: Python (essential), SQL, Bash

 Ideal Candidate Profile:

• 4+ years of experience in machine learning, with at least 2 years in biotech, healthcare, or life sciences
• Strong understanding of biological data types (e.g., DNA/RNA sequences, protein structures, cell images)
• Experience working with open-source bioinformatics tools and APIs
• Comfortable building end-to-end ML pipelines in cloud environments
• Familiarity with regulatory considerations in clinical or research settings (e.g., HIPAA, GxP)