Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal Machine Learning Operations Developer for AI Research

Autodesk
London
4 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Principal Data Scientist

Staff Machine Learning Performance Engineer, Inference Optimisation

Machine Learning Engineering Lead

Position Overview

The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter.

As a MLOps Developers at Autodesk Research, you will be working side-by-side with world-class AI researchers to build and scale foundation models trained on design data. You will focus on overcoming the challenges associated with large-scale model training and processing of vast amounts of diverse design data. Your expertise in distributed systems, ML infrastructure, and data engineering will be crucial in developing the next generation of ML-powered product features that will help our customers imagine, design, and make a better world.

You'll be joining a rapidly growing team working on a project that aims to revolutionize the design of nearly every aspect of the built environment. Your contributions will directly influence how designers, architects, and engineers interact with AI tools in the future.

This role is fully remote-friendly. Our team operates primarily remotely with team members distributed across the globe, with offices in London, Boston, Toronto and other locations worldwide. At Autodesk, we embrace remote work while fostering connection through regular team offsites for collaborative planning and relationship building. This balanced approach ensures you can work where you're most productive while maintaining meaningful connections with colleagues.

Responsibilities

Support AI researchers by building scalable ML training pipelines and infrastructure for foundation model development

Design efficient data processing workflows for large-scale design datasets and industry-specific file formats

Optimize distributed training systems and develop solutions for model parallelism, checkpointing, and efficient resource management

Analyze performance bottlenecks and provide solutions to scaling problems

Implement and maintain robust, testable code that is well documented and easy to understand

Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers

Present results to collaborators and leadership

Minimum Qualifications

BSc or MSc in Computer Science or related field, or equivalent industry experience

Experience with distributed systems for machine learning and deep learning at scale

Strong knowledge of ML infrastructure and model parallelism techniques, including frameworks like PyTorch, Lightning, Megatron, DeepSpeed, and FSDPProficiency in Python and strong software engineering practices

Experience with cloud services and architectures (AWS, Azure, etc.)

Familiarity with version control, CI/CD, and deployment pipelines

Excellent written documentation skills to document code, architectures, and experiments

Preferred Qualifications

Experience with AEC data formats (e.g., BIM models, IFC files, CAD files, Drawing Sets)

Knowledge of the AEC industry and its specific data processing challenges

Experience scaling ML training and data pipelines for large datasets

Experience with distributed data processing and ML infrastructure (e.g., Apache Spark, Ray, Docker, Kubernetes)

Experience with performance optimization, monitoring, and efficiency in large-scale ML systems

Experience with Autodesk or similar products (Revit, Sketchup, Forma)

The Ideal Candidate

A self-starter who can solve problems with minimal supervision while collaborating effectively with a global, remote-first team

Adaptable and creative, comfortable building new infrastructure or working within existing codebases

Thrives in ambiguous, rapidly evolving areas where learning and flexibility are essential

Excellent communicator who can convey complex technical concepts clearly to diverse audiences

#LI-JK3

Learn More

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience and geographic location. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Diversity & Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here:

Are you an existing contractor or consultant with Autodesk?

Please search for open jobs and apply internally (not on this external site).

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.