At a Glance
- Tasks: Design and train cutting-edge machine learning solutions for digital identities.
- Company: Join a dynamic team of innovative machine learning scientists in London.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on diversity and inclusion.
- Why this job: Make a real impact by tackling challenges like deepfake detection and bias mitigation.
- Qualifications: Strong experience in machine learning, coding skills in Python and PyTorch.
The predicted salary is between 50000 - 70000 £ per year.
We are looking for an Applied Scientist I to design and train cutting-edge machine learning solutions related to digital identities. Work on challenging problems in deepfake detection, bias mitigation, document understanding, anomaly detection and/or efficient machine learning.
Our Applied Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base.
What you will be doing:
- Push the frontier of research in areas such as deepfake detection, bias mitigation, fraud/anomaly detection, face matching, document understanding, and efficient on-device ML.
- Publish research results in national and international conferences and scientific journals.
- Work with product and engineering to improve our world-class identity-focused products.
Representative work:
- Implement bias-mitigation strategies to build fair face-matching and deepfake-detection models.
- Train and benchmark large-scale vision-language models for document extraction.
- Train a multi-modal document understanding model from scratch using synthetic data.
- Optimise LoRA adapter latency in PEFT/Triton.
- Profile, debug and improve model training speed on multiple GPUs.
- Create a large-scale dataset for deepfake detection.
- Experiment with multimodal models to detect fraud.
You may be a good fit if you:
- Have strong experience in machine learning and computer vision.
- Have a strong record of successfully delivering high-performance ML-driven products.
- Have a deep understanding of machine learning theory.
- Have strong coding skills in Python and PyTorch.
- Care about building fair and cutting-edge machine learning products.
Strong candidates may also have:
- Technical experience in one or more of the following areas: face matching, bias mitigation, anomaly detection, document understanding or on-device ML.
- Published at top-level machine learning conferences.
- Experience optimising (distributed) training code.
Location: London, hybrid: 3 days per week in office.
Applied Scientist I in London employer: Dormont Manufacturing Co
Entrust is an exceptional employer that fosters a collaborative and innovative work culture, particularly for the Applied Scientist I role in London. With a strong emphasis on employee growth, you will have the opportunity to publish research, work with cutting-edge technologies, and contribute to meaningful projects that enhance digital identities. The hybrid work model allows for flexibility while being part of a diverse team dedicated to pushing the boundaries of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Scientist I in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working in machine learning and computer vision. Attend meetups or webinars, and don’t be shy about sliding into DMs on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to deepfake detection, bias mitigation, or any other relevant work. This is your chance to demonstrate your coding prowess in Python and PyTorch. Make sure to highlight any published research too!
✨Tip Number 3
Prepare for interviews by brushing up on your machine learning theory and practical applications. Be ready to discuss your experience with model training and optimisation. Practise explaining complex concepts in simple terms – it shows you really understand your stuff!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search. Don’t forget to tailor your application to highlight how your skills align with the role of Applied Scientist I!
We think you need these skills to ace Applied Scientist I in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in machine learning and computer vision. We want to see how your skills align with the challenges we face, like deepfake detection and bias mitigation.
Showcase Your Projects:Include any relevant projects or research you've done, especially if they relate to document understanding or anomaly detection. We love seeing practical applications of your skills!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Tell us why you're passionate about building fair and cutting-edge ML products. Let us know how you can contribute to our team.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Dormont Manufacturing Co
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning and computer vision knowledge. Be ready to discuss specific projects you've worked on, especially those related to deepfake detection or bias mitigation. This will show that you not only understand the theory but can also apply it in real-world scenarios.
✨Showcase Your Coding Skills
Since strong coding skills in Python and PyTorch are essential, be prepared to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges beforehand. Familiarise yourself with common libraries and tools mentioned in the job description.
✨Prepare for Technical Questions
Expect technical questions that dive deep into your experience with model training, optimisation, and debugging. Think about how you would explain your approach to building fair models or improving training speeds. Use examples from your past work to illustrate your points.
✨Understand the Team's Goals
Research the company and its products, especially their identity-focused solutions. Understanding their mission and how your role as an Applied Scientist I fits into the bigger picture will help you articulate why you're a great fit for the team. Show enthusiasm for contributing to their cutting-edge projects!
