Applied Research Scientist, AI (Senior / Staff / Principal)

BYJUS LLC
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - NLP AI Research

Data Scientist

Data Scientist

Real World Data Scientist

Real World Data Scientist

Senior RF Data Scientist / Research Engineer

Applied Research Scientist, AI (Senior / Staff / Principal) City of London

Please make sure you read the following details carefully before making any applications.PermanentOn SiteOpportunity for Applied Research Scientist to develop EdTech Machine Learning.Job Description As an Applied Research Scientist, you will be an innovator and expert at exploring and solving some of the most complex problems at the cutting edge of digital learning using state-of-the-art AI technologies. You will be tasked with identifying and tackling complex problems surrounding digital product development to ultimately impact and advance BYJU'S product evolution.To be effective in this role, you will need to have a deep understanding of AI technologies, strong problem-solving skills, and the ability to work effectively with engineers, project managers, and other researchers to innovate BYJU'S products. A deep understanding of AI is needed to identify impactful problems, propose effective solutions, and influence the direction of fundamental research. Depending on your scope, you may be required to lead and motivate other scientists or engineers to develop workstreams and product concepts.Senior candidates could support or build a team of world-class AI researchers and ML engineers while leveraging their deep AI knowledge to build zero to one projects.The Successful Applicant All industry backgrounds are encouraged to apply; the key desire is wanting to make a difference to our younger generation and present an opportunity to give something back for social good.Candidates should have experience in some of the following:Masters or Ph.D. degree in computer science, or related technical, math, or scientific field.Strong knowledge and experience in applied research in AI, including but not limited to NLP, computer vision, reinforcement learning, recommender systems, or AI product development.Strong knowledge of foundational concepts in machine learning and AI.Ability to identify high-impact problems and execute complex research and development projects in AI, end-to-end, preferably shown by a proven track record of significant product impact using advanced AI technologies.Hands-on experience in building models with deep learning frameworks such as PyTorch, TensorFlow, or similar.Fluency in at least one programming language, preferably Python.Strong written and oral communication skills to communicate effectively internally within and between organizations.Nice to Have

Track record of publications in top-tier venues such as NeurIPS, ACL, ICML, EMNLP, CVPR, ICCV, etc.Open-source contributions, especially in the space of AI.

#J-18808-Ljbffr

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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

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.