Research Engineer — Applied Science (ML)

TimeTrace Labs Ltd.; Greater London; On-site; GBP 90,000 - 160,000; Full time

Job summary

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).

Responsibilities

• 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.

Skills

Advanced ML

Deep learning

Python programming

Experiment tracking

Mathematics & statistics

Education

MSc/PhD preferred in quantitative field

Tools

PyTorch

NumPy

SciPy

Pandas

Git

pytest

Weights & Biases

MLflow

TensorBoard

Job description

Bridge the gap between cutting-edge AI research and practical application. You will own the design, training,and fine-tuning of advanced machine learning models to solve complex, domain-specific problems. We valueengineering driven by first-principles thinking — someone who genuinely enjoys digging into the math of thelatest arXiv papers, exploring novel architectures, and rapidly translating theoretical breakthroughs intofunctional, high-performance prototypes.

What you'll do

• Model architecture & design: research, design, and prototype deep learning architectures tailored to our data domain — generative models, geometric deep learning, large-scale transformers.
• Experimental exploration: own rigorous experimental roadmaps — ablation studies, hyperparameter optimisation, and loss-function engineering.
• Algorithmic & compute optimisation: integrate novel optimisations (custom attention, tokenisation, layer normalisation) with an eye on memory efficiency and training stability.
• Data strategy & feature engineering: design representation, augmentation, and synthesis strategies for complex, constrained biological datasets.
• Collaborative hand-off: partner with MLOps so prototypes are built for stability and transition cleanly into scaled production.

What we're looking for

• Advanced ML & deep learning: deep theoretical and practical grasp of modern frameworks (PyTorch preferred), optimisation, and neural-network primitives.
• Scientific computing & math: strong linear algebra, calculus, probability, and statistics; fluency with NumPy, SciPy, and Pandas.
• Research translation: read an academic paper, understand its mechanics, and reproduce or adapt it in clean, modular Python.
• Software fundamentals: maintainable Python, Git, and rigorous testing with PyTest.
• Experiment tracking: Weights & Biases, MLflow, or TensorBoard to document and reproduce dense training runs.

Nice to have

An advanced degree (MSc/PhD) in a quantitative field; experience with large-scale generative models orspatial, time-series, or geometric data; open-source contributions or publications at NeurIPS, ICML, ICLR, or CVPR.