Senior Machine Learning Engineer

Dex; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Dex, in partnership with trusted collaborators, seeks an experienced engineer to own architecture, deployment, and reliability of vision and multimodal models in production. This role focuses on systems-first software engineering to solve GPU utilisation, latency, and throughput bottlenecks for real-time inference at scale.

You will join the in-house ML team in London, on-site four days a week, shaping end-to-end model serving and integration within a scalable platform.

Qualifications

• 5+ years of software engineering experience with deep expertise building and running production ML systems or model-backed distributed infrastructure at scale.
• Hands-on mastery of deep learning model serving and inference optimisation, including GPU utilisation, dynamic batching, and latency-throughput-cost trade-offs.
• Proven track record designing and operating scalable distributed systems on Kubernetes and cloud infrastructure (GCP/AWS) using Python and PyTorch.
• Demonstrated ownership taking experimental research prototypes and re-architecting them into dependable, maintainable platform features.

Responsibilities

• Architect and run low-latency serving infrastructure for heavy vision and multimodal models, delivering real-time interactive predictions directly into high-throughput annotation workflows.
• Build scalable offline inference pipelines processing millions of hours of egocentric video for robotics customers, balancing GPU cluster costs, throughput, and fault tolerance.
• Productionise agentic quality-control and active learning workflows, transforming fragile research prototypes into resilient, self-healing platform features.
• Design dynamic model integration layers that gracefully handle rapid, customer-defined ontology shifts without requiring bespoke one-off code.
• Profile and optimise end-to-end model serving performance across PyTorch, CUDA, and Kubernetes environments on AWS and GCP.

Job description

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.

The role

This company builds the multimodal data and evaluation platform powering model development for hundreds of leading AI teams, from autonomous vehicles to frontier robotics. Backed by top-tier global venture firms with significant growth funding, they provide the essential infrastructure that bridges raw visual and sensory data with production AI performance.

You will join the in-house ML team sitting firmly on the engineering side, owning the architecture, deployment, and reliability of vision and multimodal models in production. This is not a role for prompt wrapping or tweaking hyperparameters in notebooks. It is a systems-first software engineering seat where you will solve hard GPU utilization, latency, and throughput bottlenecks to deliver real-time model inference and closed-loop data feedback at massive scale.

The work

• Architect and run low-latency serving infrastructure for heavy vision and multimodal models, delivering real-time interactive predictions directly into high-throughput annotation workflows.
• Build scalable offline inference pipelines processing millions of hours of egocentric video for robotics customers, balancing GPU cluster costs, throughput, and fault tolerance.
• Productionise agentic quality-control and active learning workflows, transforming fragile research prototypes into resilient, self-healing platform features.
• Design dynamic model integration layers that gracefully handle rapid, customer-defined ontology shifts without requiring bespoke one-off code.
• Profile and optimise end-to-end model serving performance across PyTorch, CUDA, and Kubernetes environments on AWS and GCP.

What You Bring

• 5+ years of software engineering experience with deep expertise building and running production ML systems or model-backed distributed infrastructure at scale.
• Hands-on mastery of deep learning model serving and inference optimisation, including GPU utilisation, dynamic batching, and latency-throughput-cost trade-offs.
• Proven track record designing and operating scalable distributed systems on Kubernetes and cloud infrastructure (GCP/AWS) using Python and PyTorch.
• Demonstrated ownership taking experimental research prototypes and re-architecting them into dependable, maintainable platform features.
• Based in or able to relocate to London, working on-site in the office at least four days a week.

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