Applied AI Research Scientist: AEC

Merantix
London
1 day ago
Create job alert

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

Related Jobs

View all jobs

Research Scientist: AI for Quantum Chemistry

Senior Applied Scientist

AI Scientist - Agentic AI / Gen AI / LLM

AI Scientist - Agentic AI / Gen AI / LLM

NLP / LLM Scientist – Applied AI ML Lead – Machine Learning Centre of Excellence

Staff Data Scientist, Applied AI London

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.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.

Common Pitfalls Machine Learning Job Seekers Face and How to Avoid Them

Machine learning has emerged as one of the most sought-after fields in technology, with companies across industries—from retail and healthcare to finance and manufacturing—embracing data-driven solutions at an unprecedented pace. In the UK, the demand for skilled ML professionals continues to soar, and opportunities in this domain are abundant. Yet, amid this growing market, competition for machine learning jobs can be fierce. Prospective employers set a high bar: they seek candidates with not just theoretical understanding, but also strong practical skills, business sense, and an aptitude for effective communication. Whether you’re a recent graduate, a data scientist transitioning into machine learning, or a seasoned developer pivoting your career, it’s essential to avoid common mistakes that may hinder your prospects. This blog post explores the pitfalls frequently encountered by machine learning job seekers, and offers actionable guidance on how to steer clear of them. If you’re looking for roles in this thriving sector, don’t forget to check out Machine Learning Jobs for the latest vacancies across the UK. In this article, we’ll break down these pitfalls to help you refine your approach in applications, interviews, and career development. By taking on board these insights, you can significantly enhance your employability, stand out from the competition, and secure a rewarding position in the world of machine learning.