Design and build data pipelines for agent training: collection, filtering, deduplication, formatting, and versioning across text, image, and multimodal sources
Build and maintain infrastructure for efficient data loading, storage, and retrieval at scale (S3, distributed systems, streaming pipelines)
Collaborate with research scientists to translate research requirements into concrete data specifications, and iterate as experiments reveal new needs
Create evaluation datasets and benchmarks in collaboration with researchers—curating task distributions that surface real failure modes
Develop tooling for dataset construction—including human annotation workflows, synthetic data generation, and preference data collection for RLHF/DPO-style training
Own data quality: build validation frameworks, monitor for drift and contamination, and establish standards that make datasets trustworthy and reproducible
Document datasets thoroughly: provenance, known limitations, intended use cases, and versioning history
Implement comprehensive test coverage for data pipelines and ML workflows, ensuring reliability and catching regressions early
Elevate codebase quality through code reviews, refactoring, and establishing engineering best practices that help research velocity scale sustainably
Contribute to team roadmaps by identifying data bottlenecks and proposing solutions that unblock research velocity

Strong software engineering skills in Python, with experience building production-grade data pipelines and ML DevOps
Practical experience with prompt engineering—designing, testing, and refining prompts for reliable LLM/VLM outputs
Experience with ML data workflows: large-scale data processing and loading (Ray, or similar), data versioning, and format considerations for training (tokenization, batching, sharding)
Hands-on experience working with data pipelines for large-scale distributed ML training runs
Familiarity with annotation tooling and human-in-the-loop data collection (Label Studio or internal systems)
Understanding of ML training requirements—you know what "good data" looks like for LLM/VLM fine-tuning and can anticipate downstream issues
Experience loading and writing large datasets to/from cloud infrastructure (AWS) and distributed storage systems
Strong communication skills: you can work with researchers to scope ambiguous problems and translate needs into actionable plans
A collaborative approach, comfortable taking ownership and iterating quickly.

Equity packages
we want our success to be yours too
Inclusive parental leave policy that supports all parents & carers
An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally

Senior Machine Learning Engineer – Multimodal Data

Key skills