# Applied Machine Learning Engineer, EMEA

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** London, United Kingdom
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Python, Machine Learning, Generative AI, Supervised Fine-Tuning, AI Infrastructure, Enterprise Infrastructure, Model Serving, Low-Latency Inference, Machine Learning Models
**Posted:** 2026-07-23

> Build, fine-tune, and deploy machine learning solutions and AI-powered applications for customers while improving the internal ML platform. The role requires 5+ years of software engineering experience, strong Python skills, and customer-facing technical project leadership.

## Job Description

## Responsibilities
- Collaborate directly with the GTM team, including Account Executives and Solutions Architects, to ensure smooth integration and successful deployment of machine learning solutions.
- Build and present compelling demos and proofs of concept (PoCs) that demonstrate AI technology capabilities.
- Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.
- Contribute to the internal machine learning platform by adding features and resolving bugs.
- Integrate and enable new machine learning models in the existing platform or client environments.
- Improve the performance, efficiency, and scalability of deployed models and applications.
- Work closely with partners to enable joint AI solutions and ensure seamless collaboration.

## Requirements
- Bachelor's degree in Computer Science, Engineering, or a related technical field.
- 5+ years of experience in a software engineering role, preferably in customer-facing roles.
- Strong coding skills, preferably with proficiency in Python.
- Ability to lead and execute complex technical projects focused on customer success.
- Strong interpersonal and communication skills, with the ability to work effectively in dynamic, cross-functional teams.

## Nice-to-haves
- Master's degree in Computer Science, Engineering, or a related technical field.
- Experience working in a startup or fast-paced environment.
- Hands-on experience fine-tuning machine learning models, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF or RFT).
- Solid understanding of generative AI, machine learning principles, and enterprise infrastructure.

## Compensation and benefits
- Opportunity to tackle challenges in AI infrastructure, including low-latency inference and scalable model serving.
- Work with emerging technology that helps businesses and developers use AI globally.
- Direct ownership and impact in a fast-growing team.
- Collaboration with experienced engineers and AI researchers.

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