Applied Machine Learning Engineer, EMEA

The Role

As an Applied Machine Learning Engineer, you are the technical owner of a customer engagement. This is what is globally known as the Forward Deployed Engineer role; at Fireworks in EMEA it carries the AMLE title. You embed with a small number of accounts, work in their environment, and own whatever stands between them and production on Fireworks: the model, the serving path, the integration, the performance. You scope the work before signature and deliver what you scoped. Depth over breadth: few accounts, owned end to end.

What we value most is first principles thinking: reasoning about unfamiliar problems from the ground up rather than from playbooks. You will be evaluated on this directly in the interview process, and strength here can outweigh gaps elsewhere in your background.

Key Responsibilities

• Own Engagements: Take customer deployments from scoping through production, as the single accountable technical owner.

• Embed: Work directly inside customer teams; understand their domain, data, and constraints well enough to make decisions they trust.

• Train and Tune: Run fine-tuning and post-training work where the engagement needs it, from data to evaluation to a model serving in production.

• Optimize: Drive serving performance and cost on Fireworks: model choice, deployment shape, and inference optimization.

• Scope Honestly: Assess what is achievable before we commit, and inherit what you scope.

• Product Signal: Feed what customers need back to product and engineering, with enough precision to be actionable.
  

Minimum Qualifications

• Spikes in two or all three areas.

• For the ML spike: formal training (PhD, PhD in progress, or a strong MSc) and experience post-training LLMs specifically.

• For the infrastructure spike: experience with GPU infrastructure, distributed serving, or inference engines.

• Founder or founding engineer experience.

• Experience in a startup or fast-paced environment.