Head of AI Autonomy - Principal Architect

London
9 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Data Engineer (18 Months FTC)

Data Engineer (18 Months FTC)

Data Scientist Placement

We are seeking an exceptional Principal Engineer with expertise in Robotics, Perception, Navigation, and Planning, specializing in hands-on integration of these systems with VLA/VLM/LLM and Knowledge Bases to achieve true Robot Autonomy. This position involves pioneering the adoption and development of recent AI and Robotics innovations and a full-stack range of responsibilities from writing code and model training to strategic company-level decisions.

Responsibilities

Establish, conduct, and own the adoption of recent advancements in AI/ML and robotics.
Deliver true Robot Autonomy by integrating capabilities provided by Reasoning, Navigation and Perception teams.
Initiate and drive strategic level partnerships with different robotics companies and universities to enrich our Robot capabilities.
Own the long-term vision for End-to-End Autonomous Self-Aware Robotic systems.
Drive information flow and infrastructure design and integration across multiple teams.Expertise

MS or PhD in Robotics, Computer Science, or a related field.
Proven track record of publications and projects in the field of AI and Robotics.
Proficiency in Robotic Autonomy and VLA/VLM/LLM/RT-X type solutions or alternatives for Robotic World Modelling.
Recent hands-on experience with Python, cloud platforms, DBs, and ML.
Demonstrated ability to conduct research and develop new solutions inside and outside a Simulated Environment.
Solid CS background in data structures, algorithms, system design, deep learning, probability theory.
Comprehensive knowledge of Robotic Navigation, Perception, and Reasoning capabilities throughout the industry.Preferred Qualifications:

Advanced skills in Semantic Mapping, Knowledge Bases, SLAM.
Passion for AI-driven innovation and problem-solving in complex systems.
Expertise in strategic decision-making.
A thoughtful approach combined with excellent collaboration and interpersonal skills.
Strong background in diverse academic fields.Benefits

High competitive salary + stock options.
23 calendar days of vacation per year.
Flexible working hours.
Opportunity to work on the latest technologies in AI, Robotics, Blockchain and others.
Startup model, offering a dynamic and innovative work environment.
Proactive Global is committed to equality in the workplace and is an equal opportunity employer.
Proactive Global is acting as an Employment Business in relation to this vacancy

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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.