Member of Technical Staff | Diffusion Models | Flow Matching | Python | Pytorch | Machine Learning | Hybrid, London

Enigma; Greater London; Hybrid; GBP 120,000 - 150,000; Full time

Benefits offered by this job

Pension contributions

Generous annual leave

Hybrid working arrangements

Opportunities for international travel

Collaborative environment focused on技术

Job summary

Enigma, a London-based AI research company, seeks a hybrid Member of Technical Staff to advance generative models and deploy them into customer environments. You will bridge research and production, collaborating with ML researchers, software engineers and domain specialists to deliver impactful scientific and industrial applications.

The role blends research and engineering, requiring hands-on experience with modern generative models, scalable inference and API-based serving in cloud

Qualifications

• Strong background in ML with generative modelling.
• Contributions through research publications, open-source software, or production ML systems.
• Deep understanding of generative model architectures, training methodologies and inference.

Responsibilities

• Develop deep understanding of the company's generative models and their capabilities.
• Collaborate with researchers and engineers within a shared codebase while maintaining high engineering standards.
• Deploy, integrate and serve models within customer production environments.
• Adapt and fine-tune models to meet customer-specific requirements.
• Build ML data pipelines supporting inference, evaluation and feedback workflows.
• Ensure deployments meet security, performance and reliability requirements.
• Work closely with customers to understand tech requirements and deliver solutions.
• Act as a trusted technical advisor throughout customer engagements.

Skills

Generative modelling

Machine learning engineering

Model deployment

Customer delivery

Distributed training

Tools

Python

PyTorch

APIs

Cloud infrastructure

Job description

Member of Technical Staff | Diffusion Models | Flow Matching | Python | Pytorch | Machine Learning | Hybrid, London

About the Role

We are looking for a Member of Technical Staff with deep expertise in generative machine learning to work at the interface between cutting-edge AI models and the organisations that rely on them. You will join an interdisciplinary team of machine learning researchers, software engineers and domain specialists, helping deploy, adapt and optimise advanced generative models for real-world scientific and industrial applications.

This is a hybrid research and engineering role. You will combine a deep understanding of modern generative models with the practical skills needed to integrate them into production environments and deliver measurable value for customers.

About the Company

We are an AI research company developing state-of-the-art generative models for scientific discovery. Our team combines expertise in machine learning, software engineering and applied science to build technologies that accelerate research and innovation across life sciences and related industries.

We value scientific excellence, curiosity, collaboration and continuous learning. Our team works across multiple international locations and encourages knowledge sharing, interdisciplinary thinking and close collaboration.

We're looking for people who enjoy solving challenging technical problems and are motivated by the opportunity to create meaningful real-world impact.

About You

• Strong background in machine learning, with significant experience in generative modelling.
• Demonstrated contributions through impactful research publications, widely adopted open-source software, or production ML systems.
• Deep understanding of generative model architectures, training methodologies and inference behaviour.

Machine Learning Engineering

• Experienced in developing robust, maintainable and well-tested ML software.
• Comfortable using version control, code review and collaborative software development practices.
• Experience deploying and serving large models via APIs and cloud infrastructure.
• Familiar with distributed training and inference across modern hardware accelerators.

Customer Delivery

• Enjoy working directly with customers and delivering technical solutions.
• Able to communicate complex machine learning concepts clearly to both technical and non-technical audiences.
• Focused on successful project delivery and long-term customer outcomes.

Performance Optimisation

• Strong understanding of the interaction between ML frameworks, hardware and data pipelines.
• Experienced in optimising training and inference performance for scalability, reliability and cost efficiency

.

Mindset

• Curious, adaptable and motivated by solving difficult problems.
• Comfortable balancing deep technical work with customer-facing responsibilities.
• Passionate about applying AI to meaningful scientific or technical challenges.

Preferred Experience

While not required, experience in one or more of the following would be beneficial:

• Computational biology, bioinformatics or other scientific machine learning applications.
• Production enterprise software, including security, compliance and reliability requirements.
• Academic or professional background in a scientific discipline such as biology, chemistry, physics or a related field.

Responsibilities

• Develop an in-depth understanding of the company's generative models, including their capabilities and limitations.
• Collaborate with researchers and engineers within a shared codebase while maintaining high engineering standards.
• Deploy, integrate and serve models within customer production environments.
• Adapt and fine-tune models to meet customer-specific requirements.
• Build ML data pipelines supporting inference, evaluation and feedback workflows.
• Ensure deployments meet security, performance and reliability requirements.
• Work closely with customers to understand technical requirements and deliver solutions.
• Act as a trusted technical advisor throughout customer engagements.
• Support customers in applying AI models to domain-specific use cases and incorporate learnings into future model improvements.
• Gather customer feedback and communicate insights to research, product and engineering teams.
• Produce technical documentation, implementation guides and best practices.
• Travel to customer sites when required.

Professional Development

• Stay current with advances in machine learning, model serving and cloud technologies.
• Develop domain knowledge relevant to customer applications.
• Participate in technical knowledge sharing and internal learning initiatives.
• Attend and contribute to industry conferences and research events.

We offer a competitive compensation and benefits package, including:

• Pension contributions
• Generous annual leave and family-friendly policies
• Hybrid working arrangements
• Opportunities for international travel
• A collaborative environment focused on technical excellence and innovation

We welcome applicants from all backgrounds and are committed to building an inclusive workplace that values diverse perspectives, experiences and skills.

Member of Technical Staff | Diffusion Models | Flow Matching | Python | Pytorch | Machine Learning | Hybrid, London