• Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). • Implement scalable data preprocessing and augmentation pipelines. • Help develop embedding-based search tools and 'active learning' workflows. • Help build and maintain dashboards to monitor model health, data drift, and system performance. • Follow software engineering standards for ML code and participate in code reviews. • Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable engineering solutions.

• You'll own the productionisation of our machine learning work end to end: real-time serving against SLAs, a standardised CI/CD path to production, GenAI and RAG system deployment, and the monitoring infrastructure that catches drift and quality degradation before business KPIs move. • You'll work on a GCP-native stack — Vertex AI, BigQuery, dbt, Airflow, and Terraform — on problems that matter from day one, with the autonomy to shape how the function scales and who joins it next. • You'll collaborate closely with Data Scientists, Platform, and Engineering, acting as both a technical owner and an advocate for Data Science needs across the organisation.

Role Description As a Senior ML / MLOps Engineer, you'll be a foundational hire in our new Data Science & Applied AI function — working in close partnership with our Data Scientists to take intelligent systems from prototype to production, and keep them performing once they're live. - Own the productionisation of our machine learning work end to end: - Real-time serving against SLAs - A standardised CI/CD path to production - GenAI and RAG system deployment - Monitoring infrastructure that catches drift and quality degradation before business KPIs move - Work on a GCP-native stack — Vertex AI, BigQuery, dbt, Airflow, and Terraform - Collaborate closely with Data Scientists, Platform, and Engineering - Act as both a technical owner and an advocate for Data Science needs across the organisation Qualifications - A track record of putting models into production alongside Data Scientists — classic ML, LLM/GenAI, or both - Deep understanding of the ML lifecycle from prototype to production, including real-time serving, feature stores, autoscaling, and blue/green deployments - Strong Python and SQL skills - Hands-on GCP experience, especially Vertex AI, Kubeflow pipelines, and BigQuery — or strong fluency on an equivalent cloud with a clear ability to get to Vertex fast - Solid CI/CD and production-monitoring experience, including model registries, versioning, drift detection, and automated retraining - Conversational-level English language skills Requirements - Experience with LLM evaluation, observability tooling, or RAG systems in production - Experience defining MLOps standards across multiple teams - Exposure to cost and latency management for GenAI systems Benefits - This is a fully remote position across the UK, Spain, and Poland. - Competitive salary plus bonus. - Private health cover, life insurance, pension, and enhanced parental leave — with specifics tailored to your location. - A generous holiday allowance plus public holidays. - Access to a global learning and development programme, wellbeing support, and discounts across partner platforms.

Role Description Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. - Design challenging, domain-relevant tasks and write accurate, well-structured solutions to MLOps and ML systems problems. - Evaluate MLOps tasks and solutions and provide clear, written technical feedback. - Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization. - Hands-on production experience with JAX at scale. - Experience writing or optimizing custom GPU kernels using Pallas or Triton. - Demonstrable career progression. - Ability to engage reliably for at least 40 hours/week during weekdays. - Strong written communication skills and the ability to explain complex technical decisions clearly. Company Description