Applied AI Research Scientist: AEC

Merantix
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Psychometric Data Scientist

Senior Data Scientist

Data Scientist

Research Software Engineer

Machine Learning Engineer, Amazon Studios AI Lab

Machine Learning Engineer (UK)

Job Requisition ID #25WD85984

Applied AI Research Scientist: AEC

Position Overview

As an Applied AI Research Scientist at Autodesk Research, you will be doing applied research that will help our customers imagine, design, and make a better world.

We are a team of scientists, researchers, engineers, and designers working together on projects that range from learning-based design systems, computer vision, graphics, robotics, human-computer interaction, sustainability, simulation, manufacturing, architectural design, and construction.

This role will report to a Manager of Research Science in the AI Lab.

Responsibilities

  1. Develop new ML models and AI techniques
  2. Lead on research projects within a global team
  3. Review relevant AI/ML literature to identify emerging methods, technologies, and best practices
  4. Explore new data sources and discover techniques for best leveraging data

Minimum Qualifications

  1. A Masters or PhD in a field related to AI/ML such as: Computer Science, Mathematics, Statistics, Physics, Linguistics, Mechanical Engineering, Architecture, or related disciplines
  2. Strong background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, and deploying production-ready solutions)
  3. Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.)
  4. Familiarity with PyTorch, TensorFlow, JAX, or similar frameworks
  5. Strong coding abilities in Python

Preferred Qualifications

  1. Experience in the Architecture, Engineering, and/or Construction domains, including expertise with industry-specific data formats (e.g., BIM models, IFC files, AEC Contract Documents and Drawings such as Drawing Sets, Specifications, or Shop Drawings)
  2. Knowledge of structured data representation and management in AEC workflows (building information modeling, structural specifications, project documentation)
  3. 2D & 3D Generative AI
  4. LLMs and Natural Language Processing
  5. Multi-modal deep learning and/or information retrieval
  6. Computational geometry and geometric methods (e.g. shape analysis, topology, differential geometry, discrete geometry, functional mapping, geometric deep learning, graph neural networks)
  7. Publication track record in machine learning conferences and/or journals
  8. Significant post-graduate research experience, or 5 or greater years of work experience (actual job title/position will be commensurate to experience)

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, we also have a significant emphasis on discretionary annual cash bonuses, commissions for sales roles, stock or long-term incentive cash grants, and a comprehensive benefits package.

Diversity & Belonging
We take pride in cultivating a culture of belonging and an equitable workplace where everyone can thrive. Learn more here:https://www.autodesk.com/company/diversity-and-belonging

Are you an existing contractor or consultant with Autodesk?

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

J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.