At a Glance
- Tasks: Design and modify training loops for large-scale ML models and study learning dynamics.
- Company: Join a leading market innovator in advanced machine learning research.
- Benefits: Competitive compensation, unlimited compute resources, and full visa sponsorship for international talent.
- Why this job: Own your research threads and redefine how models are trained in a collaborative environment.
- Qualifications: Post-graduate degree in ML or related fields with experience in leading research labs.
- Other info: Work in a small, senior team with deep technical autonomy on the East Coast.
The predicted salary is between 36000 - 60000 £ per year.
Sentiro Partners is working with a head-turning market leader. We’re building models that are trained under real constraints, not academic ones.
This role sits inside a focused, senior research group focused on how large models learn, not just how they perform on benchmarks. You will have direct ownership over training dynamics, optimization behaviour, and failure modes of large-scale learning systems.
This is not a product ML role. This is not post-hoc evaluation. This is not incremental fine-tuning work.
If your background is in frontier labs, advanced ML research, or deep systems-aware model training, this role is designed to feel familiar, likely more demanding. You have likely published at top conferences, such as NeurIPS, ICML or ICLR.
What you’ll work on:
- Designing and modifying training loops for large-scale models
- Studying learning dynamics, optimization behaviour, and generalization
- Stress-testing models under distribution shift and adversarial conditions
- Building signals that distinguish real progress from benchmark overfitting
- Collaborating with researchers who care about first-principles understanding, not paper counts
What makes this different:
- You own entire research threads, end to end
- You can change how models are trained, not just how they’re evaluated
- Feedback cycles are tight and unforgiving
- Success is defined by robustness and signal quality, not publications
Who this is for:
You might be a fit if you’ve worked on:
- Large-scale model training or optimization
- Reasoning, generalization, or learning dynamics
- Systems-aware ML (where infra and modeling meet)
- Frontier-lab research that felt too abstract or too slow
We are intentionally not optimizing for:
- Narrow domain specialists
- Pure infra engineers
- Product-driven ML roles
Environment:
- Small, senior research team
- Deep technical autonomy
- In-office collaboration (East Coast)
- Compensation and resources competitive with top-tier research labs
- Unlimited compute
Requirements:
- Post-graduate degree in machine learning, computer science, an NLP adjacent discipline, or commercial experience developing novel machine learning algorithms.
- Research depth e.g. demonstrable experience in internships or employment in leading research labs or research environments.
Location: East Coast USA. This role is being advertised across the US. Full visa sponsorship will be provided for international talent.
Foundational ML Researcher - Learning & Training Dynamics - USA in City of London employer: Sentiro Partners
Contact Detail:
Sentiro Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Foundational ML Researcher - Learning & Training Dynamics - USA in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in your field on LinkedIn or at conferences. A personal connection can make all the difference when it comes to landing that dream role.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a presentation that highlights your past projects and research. This is your chance to demonstrate your expertise in large-scale model training and optimization.
✨Tip Number 3
Practice makes perfect! Get ready for those tough interviews by doing mock sessions with friends or mentors. Focus on explaining complex concepts clearly, as you’ll need to showcase your understanding of learning dynamics.
✨Tip Number 4
Apply through our website! We want to see your application directly, so don’t hesitate to submit your details there. It’s the best way to ensure we notice your talent and fit for the role.
We think you need these skills to ace Foundational ML Researcher - Learning & Training Dynamics - USA in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in large-scale model training and optimization. We want to see how your background aligns with the specific requirements of the role, so don’t hold back on showcasing your relevant projects and publications!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about learning dynamics and how your previous work has prepared you for this role. We love seeing genuine enthusiasm and a clear understanding of what we do.
Showcase Your Research Impact: If you've published at top conferences like NeurIPS or ICML, make sure to mention those! Highlighting your research impact will help us see your potential contribution to our team. We’re looking for candidates who can own research threads and drive innovation.
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’re considered for the role. Plus, it shows you’re proactive and keen to join our team!
How to prepare for a job interview at Sentiro Partners
✨Know Your Research Inside Out
Make sure you can discuss your previous research in detail, especially any work related to large-scale model training or learning dynamics. Be prepared to explain your methodologies and the impact of your findings, as this role demands a deep understanding of complex concepts.
✨Demonstrate Problem-Solving Skills
Prepare to tackle hypothetical scenarios during the interview that test your ability to think critically about optimization behaviour and failure modes. Practise articulating your thought process clearly, as they’ll want to see how you approach real-world challenges.
✨Showcase Collaboration Experience
Since this role involves working closely with other researchers, be ready to share examples of past collaborations. Highlight how you contributed to team projects and how you navigated differing opinions to achieve common goals.
✨Familiarise Yourself with Current Trends
Stay updated on the latest advancements in machine learning, particularly those related to training dynamics and generalisation. Being able to discuss recent papers or breakthroughs will show your passion for the field and your commitment to staying at the forefront of research.