EMEA Frontline Applied ML Engineer – Production & Deployment

Fdetimes; Greater London; Ibrido; GBP 90.000 - 150.000; Tempo pieno

Descrizione del lavoro

Fireworks seeks an Applied Machine Learning Engineer (Forward Deployed Engineer) to own customer engagements end-to-end, embedding with client environments and delivering production-ready AI deployments.

You will work with a small number of accounts, applying first-principles thinking to build, train, and serve tailored AI models while balancing performance, cost, and reliability. Strong ML, software, and systems skills are essential in this fast-paced startup setting.

Competenze

• Two or three spikes across software, ML, infra, or customer engagement.
• Formal training (PhD, PhD in progress, or strong MSc) and experience post-training LLMs.
• Experience with GPU infrastructure, distributed serving, or inference engines.

Mansioni

• Own engagements: take customer deployments from scoping through production.
• Embed inside customer teams to understand domain, data, and constraints.
• Train and tune: perform fine-tuning and post-training work as needed.
• Optimize: drive serving performance and cost, including model choice and deployment shape.
• Scope honestly: assess what is achievable before committing.
• Provide product signal: feed customer needs back to product and engineering.

Conoscenze

Software engineering

Machine learning

Infrastructure and performance

Customer engagement

Formazione

PhD in ML or related field

Strumenti

Python

GPU infrastructure

Distributed serving

Inference engines

Descrizione del lavoro

Fireworks seeks an Applied Machine Learning Engineer (Forward Deployed Engineer) to own customer engagements end-to-end, embedding with client environments and delivering production-ready AI deployments.

You will work with a small number of accounts, applying first-principles thinking to build, train, and serve tailored AI models while balancing performance, cost, and reliability. Strong ML, software, and systems skills are essential in this fast-paced startup setting.