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# Applied Machine Learning Engineer

- **Company:** Fireworks Ai
- **Location:** New York, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Applications, Software Bug Management, Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Software Engineering, Reinforcement Learning, Build Management, Information Technology, Software Coding
- **Published:** August 28, 2026
- **Apply:** https://www.dice.com/job-detail/431c271b-0ce6-4ca0-9a54-8b63041dc9fc

## About the Role

* Bachelor's degree in Computer Science, Engineering, or a related technical field.
 * 5+ years of experience in a software engineering role, with a strong preference for customer-facing roles.
 * Robust coding skills required, preferably with proficiency in Python.
 * Demonstrated ability to lead and execute complex technical projects with a focus on customer success.
 * Strong interpersonal and communication skills; ability to thrive in dynamic, cross-functional teams., * 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.

## Description

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions., * Customer Success: Collaborate directly with the GTM team (Account Executives and Solutions Architects) to ensure smooth integration and successful deployment of ML solutions.

 * Demo / Proof of Concept (PoC): Build and present compelling PoCs that demonstrate the capabilities of our AI technology.
 * Application Build: Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs.
 * Platform Features / Bug Fixes: Contribute to the internal ML platform, including adding features and resolving issues.
 * New Model Enablements: Integrate and enable new machine learning models into the existing platform or client environments.
 * Performance Optimizations: Improve system performance, efficiency, and scalability of deployed models and applications.
 * Partnership Enablement: Work closely with partners to enable joint AI solutions and ensure seamless collaboration.


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