Machine Learning Infrastructure Engineering Lead - UK

Symbolica AI; Greater London; On-site; GBP 60,000 - 100,000; Full time

Job summary

Join a transformative project at an innovative firm focused on AI. As a Machine Learning Infrastructure Engineering Lead, you will design and optimize the infrastructure that powers groundbreaking machine learning experiments. This role offers the chance to work at the intersection of research and engineering, ensuring the R&D team has the tools necessary to push the boundaries of AI. Collaborate with top-tier machine learning scientists and engineers, and contribute to building scalable systems that revolutionize the AI landscape. This is an exciting opportunity to make a significant impact in a rapidly evolving field.

Benefits

Equity options

Competitive compensation

Flexible working hours

Professional development opportunities

Qualifications

• 5+ years in software engineering or infrastructure roles.
• Experience in machine learning infrastructure is a must.

Responsibilities

• Lead the infrastructure for large-scale machine learning workflows.
• Develop tools for experiments ensuring scalability and reproducibility.

Skills

Machine Learning

Infrastructure Engineering

Cloud Platforms (AWS, GCP, Azure)

CI/CD Pipelines

Containerization (Docker, Kubernetes)

Problem-Solving

Education

Bachelor’s degree in Computer Science

Master’s degree in Engineering

Tools

AWS

GCP

Azure

Docker

Kubernetes

Job description

Machine Learning Infrastructure Engineering Lead - UK

London

The Next Dimension in Structured Reasoning...

Symbolica is building the new foundation for enterprise-scale AI — controllable, interpretable, reliable, and secure. This is an opportunity to be part of a transformative project and make significant contributions to the field of AI.

Symbolica was founded in 2022 and recently raised over $30M from Khosla, General Catalyst, Buckley Ventures, Abstract Ventures, Day One Ventures, and other prominent Silicon Valley venture capital firms, to advance machine reasoning. We’re a well-resourced, nimble team with a drive to deliver exceptional AI capabilities in short order.

We are looking for a Machine Learning Infrastructure Engineering Lead to design, build, and optimize the infrastructure and tools that enable our research and development efforts. In this role, you will lead the development of scalable infrastructure that powers our machine learning experiments, model training, and deployment. You’ll work at the intersection of research and engineering, ensuring our R&D team has the robust platform they need to push the boundaries of AI, working with our GPU vendors, cloud providers, and on-prem servers.

Responsibilities

1. Lead the implementation and management of infrastructure for large-scale machine learning workflows, including training systems and model deployment.
2. Develop tools and frameworks to support the global team’s experiments and ensure reproducibility and scalability.
3. Optimize compute resources and ensure efficient use of cloud and on-premises hardware for training and inference.
4. Build and maintain CI/CD pipelines tailored for machine learning development.
5. Collaborate closely with machine learning scientists, researchers, and engineers to identify and address infrastructure needs.

Preferred Qualifications

1. Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
2. 5+ years of experience in software engineering or infrastructure roles, with at least 2 years in machine learning infrastructure.
3. Proficiency in cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes).
4. Experience building CI/CD pipelines for machine learning workflows.
5. Exceptional problem-solving skills, with the ability to design and implement robust, scalable systems.

Details

This role is based in our Shoreditch office in London.

We offer competitive compensation, including equity. Salary and equity levels are commensurate with experience.

At Symbolica.ai, our mission is to revolutionize the AI landscape by creating machine learning solutions that are radically transparent, highly efficient, and meticulously compliant. We are building deep learning models which manipulate structured data, learn algebraic structure in it, and do so with an interpretable and verifiable logic. We are now hiring exceptional talent who are helping us make this a reality.

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