ML Systems Engineer — AI for High-Value Manufacturing

Nexus Additive Ltd.; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Competitive salary + equity ownership

Healthcare package

25 days holiday + bank holidays

Central London location (in-person)

Team events and learning opportunities

Job summary

Nexus, a London-based AI and manufacturing startup, is seeking an ML engineer to deploy and optimize large-scale AI systems. You will work on multi-modal data processing and real-time inference for critical components in aerospace, medical, and automotive contexts.

The role focuses on deploying novel computational approaches, working with state-of-the-art models, and collaborating with a software team to solve deployment challenges in constrained environments. In-person at Central London offices.

Qualifications

• Bachelor's degree or equivalent in CS/Engineering or related field.
• 3+ years of hands-on experience building and deploying real-world AI systems.
• Experience deploying ML inference in C++ (ONNXRuntime, TensorRT, libtorch).
• Proven deep learning knowledge (CNNs, time-series, transfer learning).
• Exceptional problem-solving with practical bias.
• Eligible to work in the UK.

Responsibilities

• Deploy multi-modal data processing and inference pipelines (image, time-series, point clouds).
• Work with large AI models requiring novel deployment approaches.
• Collaborate with software team on deployment and inference in resource-constrained environments.

Skills

AI deployment

C++ inference

Deep learning

Problem solving

UK eligibility

Education

Bachelor's degree in CS/Engineering

Tools

ONNXRuntime

TensorRT

Libtorch

Job description

Nexus, a London-based AI and manufacturing startup, is seeking an ML engineer to deploy and optimize large-scale AI systems. You will work on multi-modal data processing and real-time inference for critical components in aerospace, medical, and automotive contexts.

The role focuses on deploying novel computational approaches, working with state-of-the-art models, and collaborating with a software team to solve deployment challenges in constrained environments. In-person at Central London offices.