Machine Learning Research Scientist | Generative Models | Protein Design | Deep Learning | Python | Hybrid, LDN

Machine Learning Research Scientist | Generative Models | Protein Design | Deep Learning | Python | Hybrid, LDN

In order to make an application, simply read through the following job description and make sure to attach relevant documents.

We are looking for multiple highly skilled machine learning researchers with strong expertise in generative modeling is sought to join an interdisciplinary team of machine learning experts, protein engineers, and biologists. The team collaborates to transform how biology is controlled and diseases are cured. The role involves architecting innovative generative models aimed at designing new proteins that demonstrate functionality in wet lab assays.

This company specializes in developing generative AI models for synthetic biology, focusing on designing and reprogramming biological systems, including gene editing technologies to enable treatments for complex genetic diseases. Operating at the intersection of AI and biology, the team is driven by innovation, curiosity, and a commitment to creating significant positive global impact.

Requirements

• Expertise in generative modeling: The ideal candidate has a proven track record in machine learning, with experience leading or contributing to high-profile projects, as evidenced by widely used open-source libraries, major product launches, or impactful publications (e.g., NeurIPS, ICML, ICLR, or Nature).

• Skilled in ML development: They write robust, maintainable ML code, have proficiency in version control and code review systems, and are capable of producing high-quality prototypes and production code. They have experience running models on cloud hardware and parallelizing data and models across accelerators.

• Data engineering capabilities: The candidate is experienced in building ML data pipelines for training and evaluating deep learning models, including raw data analysis, dataset management, and scalable pipeline construction.

• Passion for optimization: They possess in-depth knowledge of ML libraries, hardware interactions, and optimization techniques for model training, inference speed, and validation metrics performance.

• Mission-driven and curious: Motivated by the opportunity to make a positive global impact, they approach problems with relentless curiosity and adaptability.

• Adaptability in dynamic environments: They thrive in fast-paced settings, achieving goals efficiently and effectively.

Desired Qualifications

• Experience in computational biology or protein design: Experience with ML-driven projects in biology is advantageous.

• Natural science background: Academic training in fields like physics, biology, or chemistry is a plus.

Key responsibilities

Develop machine learning models with real-world applications (~90%):

• Curate and manage training and evaluation data.
• Design and implement ML evaluation metrics aligned with organizational goals.
• Rapidly prototype generative models and perform detailed analyses of their performance.
• Collaborate with researchers, engineers, and designers, maintaining a high-quality codebase.
• Support the maintenance of compute and ML infrastructure.
• Coordinate with biology teams for wet lab testing campaigns and conduct model inferences for biological target testing.
• Incorporate feedback from wet lab results to refine and improve models.

Engage in self-development (~10%):

• Stay updated on the latest ML research and advancements.
• Develop a strong understanding of protein and cell biology.
• Share knowledge by organizing and presenting in reading groups or at conferences.

Excellent compensation – six figures+ & equity

Hybrid Working – 3 days p/w onsite. Central London

Permanent position

If you are interested in finding out more about this hire please reach out to tom@enigma-rec.ai for immediate consideration.

Machine Learning Research Scientist | Generative Models | Protein Design | Deep Learning | Python | Hybrid, LDN

Remote working/work at home options are available for this role.