Jobgether
AI Researcher — Training Optimization
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
AI Researcher — Training Optimization
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Researcher — Training Optimization based in the United Kingdom.
This research-focused role offers the opportunity to improve the efficiency, stability, and scalability of large-scale AI model training.
You will work at the intersection of machine learning research and systems engineering to develop practical training optimization techniques. Your work will target areas such as convergence, training cost, memory efficiency, numerical stability, and model quality. You will design rigorous experiments, analyze large-scale results, and turn research insights into measurable improvements. The role also provides opportunities to publish research and contribute to the broader machine learning community. You will collaborate closely with infrastructure and inference teams to ensure research decisions translate effectively into real-world performance.
This is an ideal environment for an independent researcher who enjoys solving challenging problems and turning novel ideas into practical results.
Accountabilities
- Design, implement, and evaluate novel training optimization techniques for large-scale neural networks, including optimization algorithms, schedulers, normalization methods, and curriculum strategies.
- Investigate approaches for improving training efficiency, stability, convergence speed, and overall model quality across long training runs and large datasets.
- Research and implement techniques involving optimizer and scheduler innovations, mixed- and low-precision training, memory-efficient training, gradient noise reduction, scaling laws, and convergence analysis.
- Explore training-time regularization and robustness techniques that can improve model performance and reliability.
- Design and execute large-scale experiments, analyze results rigorously, and translate findings into actionable improvements to training systems and methodologies.
- Author or co-author research papers, technical reports, blog posts, and other forms of high-quality technical communication.
- Collaborate with infrastructure and inference engineering teams to connect training decisions with real-world production performance and constraints.
- Independently investigate emerging research directions and contribute to core model training decisions.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Requirements
- Strong background in machine learning research, with particular expertise in training dynamics, optimization, and large-scale model training.
- Demonstrated experience training large neural networks, including large language models, multimodal models, or other large sequence models.
- Publication experience at machine learning or natural language processing venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, COLM, arXiv, or through equivalent high-quality open research.
- Strong understanding of optimization theory and practice, backpropagation, gradient flow, training stability, distributed training, and large-batch training.
- Strong proficiency in Python and experience with modern machine learning frameworks, particularly PyTorch.
- Ability to independently formulate research questions, design experiments, interpret complex datasets, and reason from empirical evidence.
- Experience with non-standard architectures, such as RNN variants, long-context models, or hybrid systems, is a plus.
- Experience optimizing large-scale GPU training using technologies such as FSDP, ZeRO, or custom kernels is desirable.
- Contributions to open-source machine learning projects or research codebases are advantageous.
- Ability to work effectively in a fast-moving, ambiguous environment while maintaining strong scientific rigor and technical quality.
- Strong communication and collaboration skills, with the ability to explain complex research findings and work effectively across research and engineering teams.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Benefits
- Full-time opportunity within a research-focused AI environment.
- Remote working arrangement with the flexibility to contribute from locations worldwide.
- Direct influence over core model training strategies and technical decisions.
- Freedom to investigate novel research ideas and publish meaningful research.
- Direct access to large-scale experiments and real-world production constraints.
- Opportunity to work on advanced problems involving training optimization, efficiency, convergence, and model quality.
- Close collaboration with infrastructure and inference engineering teams.
- Opportunity to contribute to research papers, technical reports, and open technical work.
- Small, senior-level team that values deep technical thinking and thoughtful execution.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
#LI-CL1
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location