Applied Machine Learning Engineer (EMEA)

Join Fireworks, a fast-growing AI infrastructure company, as an Applied Machine Learning Engineer. In this role, you will be the technical owner of customer engagements, working directly with customer teams to understand their domain, data, and constraints. You will be responsible for scoping work, training and tuning models, optimizing performance, and providing product feedback. This is an opportunity to tackle challenging problems at the forefront of AI infrastructure and make a direct impact on the future of AI.

Responsibilities

• 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.

Requirements

• 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

Four Spikes

• Software engineering. You ship production systems and are comfortable dropped into an unfamiliar codebase or a customer environment with a deadline. Backend and systems depth, strong Python plus at least one systems language, and fluency with AI-assisted and agentic engineering as a core part of how you build
• Infrastructure and performance. You know your GPUs: memory bandwidth, kernels, parallelism, what utilization numbers actually mean. Serving and inference optimization: quantization, speculative decoding, batching, KV cache behavior, latency vs throughput tradeoffs, multi-GPU and multi-node serving, capacity planning. You can take a deployment that works and make it fast and economical, and reason about where the bottleneck is before touching a profiler
• For the infrastructure spike: experience with GPU infrastructure, distributed serving, or inference engines

Preferred

Experience in a startup or fast-paced environment

• Machine learning. You take open models and make them better on a customer's task: SFT, LoRA and other parameter-efficient methods, reinforcement learning (RLHF, RLVR), distillation. Just as important, you design evaluations that reflect the real task and let them drive training decisions, and you know when fine-tuning is not the answer
• Customer engagement. You want to be in the room. The point of this role is creating specialized intelligence for each customer: the best model for their task, their data, their constraints. That takes real interest in the customer's problem, not just the technical one. Exceptional here looks like founder energy with customers; the floor is genuine appetite to embed, explain, and own outcomes with them
• Founder or founding engineer experience