At a Glance
- Tasks: Join our AI Lab to build and train deep learning models for trading strategies.
- Company: Adamas Knight is a leading firm pushing the boundaries of AI in finance.
- Benefits: Enjoy competitive pay, 30 days leave, flexible work culture, and daily catered lunches.
- Why this job: Work on cutting-edge technology in a collaborative environment with real-world impact.
- Qualifications: Advanced degree in ML or related field; strong programming skills in Python required.
- Other info: Diversity is valued; we encourage applicants from all backgrounds to apply.
The predicted salary is between 43200 - 72000 £ per year.
We’re looking for smart and curious individuals from industry and academia to join our client's growing AI Lab and push the boundaries of applied deep learning in trading.
On their AI team, you’ll build and train deep learning models that directly power their trading strategies, supported by a massive and rapidly expanding compute cluster (thousands of H100s/200s). The challenges here are unique: ultra-low latency, vast and noisy datasets, constantly shifting dynamics, and tight feedback loops. These constraints demand original thinking and new techniques.
Researchers, engineers, and traders work closely together (often side by side) to train models, build systems, and run live strategies. One day you might be optimising training performance across thousands of GPUs; the next, you’re analysing how a model trades in production or designing a new architecture to capture subtle market signals.
They will rely on your deep knowledge of deep learning, whether your background is in LLMs, recsys, image models, RL agents, or classical methods, to help shape the next generation of their ML-driven trading. You’ll also contribute to hiring, mentor teammates, and share insights from the broader research community through papers, internal talks, and conference travel.
We’re open to a range of backgrounds and experiences, but the ideal candidate will have:
- An advanced degree in machine learning, statistics, applied math, or a related discipline; or equivalent experience in industry applying ML to challenging problems
- Expertise in one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods
- Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow
- A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR) or competitive performance in ML challenges such as Kaggle or similar platforms
- The ability to independently formulate research questions and design experiments to answer them
- A desire to work on applied problems where real-world performance and feedback matter
What They Offer:
- Highly competitive compensation and generous performance-based bonuses
- Access to extensive compute resources, including large-scale GPU clusters
- A collaborative and intellectually stimulating research environment
- 30 days of paid leave annually
- Employer pension contributions
- Daily catered lunch and barista service
- Flexible work culture with a focus on sustainability and well-being
- Comprehensive healthcare and life insurance coverage
- Monthly team events and regular conference attendance
At Adamas Knight, we are committed to creating an inclusive culture. We do not discriminate based on race, religion, gender, national origin, sexual orientation, age, veteran status, disability, or any other legally protected status. Diversity is highly valued, and we encourage applicants from all backgrounds to apply.
Contact Detail:
Adamas Knight Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Researcher
✨Tip Number 1
Familiarise yourself with the latest advancements in deep learning and machine learning techniques. Being well-versed in current research will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with the community by attending relevant conferences or workshops. Networking with professionals in the industry can provide valuable insights and potentially lead to referrals, which can significantly boost your chances of landing the job.
✨Tip Number 3
Showcase your practical experience by working on personal projects or contributing to open-source initiatives. This hands-on experience can set you apart from other candidates and highlight your ability to apply theoretical knowledge to real-world problems.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and problem-solving scenarios related to machine learning. Familiarity with tools like PyTorch or TensorFlow is essential, so ensure you're comfortable demonstrating your skills in these areas.
We think you need these skills to ace Machine Learning Researcher
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, deep learning, and any specific tools mentioned in the job description, such as Python, NumPy, or TensorFlow. Showcase your publication record and any competitive achievements in ML challenges.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background aligns with their needs, particularly your experience with deep learning and applied problems. Mention any collaborative projects that demonstrate your ability to work in a team.
Highlight Research Experience: If you have published papers or conducted significant research, summarise this in your application. Emphasise your ability to formulate research questions and design experiments, as these skills are crucial for the role.
Showcase Problem-Solving Skills: Provide examples of how you've tackled complex problems in your previous roles or studies. This could include optimising algorithms, working with large datasets, or developing innovative solutions in machine learning.
How to prepare for a job interview at Adamas Knight
✨Showcase Your Technical Skills
Be prepared to discuss your expertise in deep learning and related areas. Highlight specific projects where you've applied techniques like reinforcement learning or non-convex optimisation, and be ready to explain your thought process and the outcomes.
✨Demonstrate Problem-Solving Abilities
Expect to face real-world problems during the interview. Think critically about how you would approach these challenges, especially those involving vast datasets and low latency. Show your ability to formulate research questions and design experiments.
✨Prepare for Collaborative Discussions
Since the role involves working closely with researchers, engineers, and traders, be ready to discuss how you collaborate in a team setting. Share examples of past experiences where teamwork led to successful outcomes, particularly in high-pressure environments.
✨Highlight Your Research Contributions
If you have a strong publication record or have participated in ML challenges, make sure to bring this up. Discuss your contributions to the research community and how they relate to the position, as this shows your commitment to advancing the field.