Applied AI Research Engineer: From Theory to Prototypes

TimeTrace Labs Ltd.; Greater London; Sur place; GBP 90 000 - 160 000; Plein temps

Résumé du poste

TimeTrace Labs Ltd. in London is seeking a highly capable ML researcher to own the design, training, and fine-tuning of advanced models for complex domain-specific problems.

You will explore cutting-edge architectures and rapidly translate theoretical breakthroughs from arXiv into functional, high-performance prototypes. Collaborating with MLOps, you will implement data strategies, run rigorous experiments, optimise compute and memory usage, and ensure smooth hand-off into scaled production.

Qualifications

• Strong theoretical and practical grasp of modern ML and deep learning.
• Solid math skills: linear algebra, calculus, probability, statistics.
• Able to read papers and reproduce or adapt methods in clean Python.
• Proficient Python, Git, and rigorous testing with PyTest.
• Experience with experiment tracking tools (Weights & Biases, MLflow, or TensorBoard).

Responsabilités

• Design and prototype deep learning architectures tailored to data domains.
• Lead experimental roadmaps with ablation studies and hyperparameter tuning.
• Implement compute optimisations focusing on memory efficiency and stability.
• Develop data representation, augmentation, and synthesis strategies.
• Collaborate with MLOps to productionise prototypes.

Connaissances

Advanced ML

Deep learning

Python programming

Experiment tracking

Mathematics & statistics

Formation

MSc/PhD preferred in quantitative field

Outils

PyTorch

NumPy

SciPy

Pandas

Git

pytest

Weights & Biases

MLflow

TensorBoard

Description du poste

TimeTrace Labs Ltd. in London is seeking a highly capable ML researcher to own the design, training, and fine-tuning of advanced models for complex domain-specific problems.

You will explore cutting-edge architectures and rapidly translate theoretical breakthroughs from arXiv into functional, high-performance prototypes. Collaborating with MLOps, you will implement data strategies, run rigorous experiments, optimise compute and memory usage, and ensure smooth hand-off into scaled production.