How your CV stacks up
Upload your CV to see how well it fits this job role
?%
The AI Accelerator
Most diseases remain poorly understood at a biological level, despite decades of research. The causal mechanisms driving many conditions are unclear, limiting our ability to identify the right targets, design interventions, and bring effective medicines to patients.
The AI Accelerator exists to change this. Based in London and part of Computational Innovation—a global organisation spanning computational biology, human genetics, data excellence, and AI—the Accelerator’s mission is to develop production-quality AI capabilities that deepen our understanding of disease biology and improve the likelihood of therapeutic success.
Approach
We apply neural-based AI methods to integrate heterogeneous, multimodal biomedical data, infer biological relationships, and embed causal thinking into our work. Our aim transcends prediction—we seek to explain and understand why disease occurs.
Explore a few examples of our implementations:
- Electronic health records and medical imaging to inform patient stratification and targeted therapies.
- Omics data for novel target discovery.
- Transcriptional change analysis for disease-causing variants.
- Simulation of target modulation effects to evaluate potential interventions.
AI Enablement
A core component of our strategy revolves around AI Enablement, which ensures technical feasibility of our ambitions. This involves:
- Provisioning integrated, multimodal biomedical data for model development.
- Managing model lifecycles, from end-to-end development to production.
- Collaborating with IT teams to optimise infrastructure and tooling.
AI Enablement empowers model builders to focus on innovation while guaranteeing real portfolio impact for downstream users.
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.
The Position: Senior MLOps Engineer
We seek a ** Senior MLOps Engineer** to join our AI Enablement team. You will play a central operational role, ensuring that the AI Accelerator’s models transition smoothly from prototyping to production—while maintaining performance reliability.
This is a hands-on operational role with critical stakes. The models you deploy and oversee will inform decision-making around indications, patient populations, and therapeutic targets. When your systems excel, science advances, and strategic choices improve—driven by data.
Core Responsibilities
- Oversee the interpretation and use of experiment tracking and model registry systems, ensuring consistent logging of training, fine-tuning runs, and model artefacts with full provenance tracking.
- Configure, run, and troubleshot distributed training and fine-tuning jobs, maximising compute efficiency and resolving technical failures at scale.
- Engage in structured model handovers with ML engineers, reviewing and signing off documentation before assuming full operational ownership of shipped models.
- Deploy and monitor model serving pipelines, making performance-informed technical decisions to meet demands of downstream users.
- Take full operational responsibility for production models, managing monitoring, retraining, and end-of-life processes.
- Uphold and enhance MLOps standards, incorporating operational insights and keeping teams current with emerging advancements.
Required Qualifications
- ** degenerative education or equivalent experience.** Significant expertise in ML infrastructure operations is expected.
- Hands-on ML training and serving expertise in production environments.
- Experience with distributed frameworks, including PyTorch Distributed, DeepSpeed, FSDP, or Ray Train.
- Proficiency in model registry and experiment tracking systems (MLflow, Weights & Biases, or alternatives).
- Familiarity with CI/CD pipelines suited to ML workflows (cloud-native services, GitHub Actions, or similar).
- Elevated understanding of cloud infrastructure (compute, storage, networking) to clarify requirements and troubleshoot infrastructure.
- Aware of large-scale model challenges, including memory resource planning and compute-scaling strategies.
- Knowledge of Infrastructure-as-Code, e.g., Terraform or cloud-native implementations.
- Collaborative experience with research and ML engineering teams, particularly as a platform operator.
- Prior exposure to massive foundation model training (in scale or workload scope).
- Biomedical AI familiarity; experience working with large-scale multimodal data clinical systems an asset.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Interview Information
Shortlisting deadline: Applications will be screened by 28 July, with interviews taking place between 28 July and 6 August.
Workplace Conditions
This is a hybrid role with approximately 3 in-office days monthly.
Why Work With Us
Boehringer Ingelheim has been accredited as a Top Employer in the UK, reflecting dedicated people practices and a commitment to supportive HR policies. Discover more about our organisation and culture on their Careers Page.
“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