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# Senior Applied Machine Learning Engineer

- **Company:** Hewlett-Packard Enterprise
- **Location:** Durham, NC, United States
- **Experience:** Expert
- **Salary:** $141,000.0 - $325,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Open Source Technology, Software Deployment, Systems Integration, LangChain, Retrieval-Augmented Generation, Apache Spark, Deep Learning, AI Coding Agents, Agentic-AI, Kubernetes, Optimization Algorithms, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** October 20, 2026
- **Apply:** https://hpe.wd5.myworkdayjobs.com/Jobsathpe/job/Durham-North-Carolina-United-States-of-America/Senior-Applied-Machine-Learning-Engineer_1216153-1

## About the Role

* PhD with at least 2 years of relevant industry experience, or the equivalent (e.g., Master's degree with 4+ years, Bachelor's with 6+ years).
 * Extensive hands-on experience applying machine learning and AI solutions in customer-facing or end-user environments.
 * Proven ability to deploy models in production, ensuring reliability and performance.
 * Experience with open-source ML/AI tools and frameworks.
 * Experience with backend programming languages (Python, Go).
 * Proficiency in developing, using, and maintaining AI agents; proven experience coding agents for automation or decision-making tasks.
 * Excellent written and verbal communication skills, especially in asynchronous collaboration.

Nice to Have:

 * Experience with AI agents and automation.
 * Knowledge of inference deployment and optimization techniques.
 * Familiarity with large-scale data pipelines.
 * Experience with retrieval-augmented systems (e.g., RAG).

## Description

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office., We are seeking an experienced Senior Applied Machine Learning Engineer with a proven track record of deploying, integrating, and leveraging machine learning and AI solutions in real-world, customer-facing environments. The ideal candidate has worked either directly with clients as part of an AI/ML solutions team or as an end-user of AI/ML products to solve practical business challenges.

Role Overview:
In this role, you will apply your hands-on experience with machine learning and AI technologies to build, optimize, and integrate solutions that address customer needs and improve product performance. You will translate complex data and AI/ML models into accessible, scalable solutions, working closely with cross-functional teams to ensure successful deployment and adoption.

Responsibilities:

Applied ML Development:

 * Design, develop, and deploy machine learning models and AI solutions that address real-world customer problems, focusing on usability, scalability, and performance.

Proof of Concept & Innovation:

 * Rapidly develop demos, POCs, MVPs, and workflows to showcase new AI/ML capabilities that could be integrated into the product or used to improve existing features based on customer feedback or market research. Work in a fast-paced environment to experiment with emerging techniques and tools, ensuring the creation of tangible, functional prototypes that demonstrate practical AI/ML solutions for real-world problems.

Integration & Deployment:

 * Develop and improve integrations of open-source ML/AI tools (e.g., MLFlow, Spark, LangChain, Kubeflow) within production environments, ensuring seamless operation on platforms like Kubernetes.

Solution Optimization:

 * Fine-tune models and algorithms for accuracy, efficiency, and scalability in production settings, including deep learning technologies.

Product & System Enhancement:

 * Translate customer requirements and industry trends into actionable AI/ML solutions that improve product features, data management, and system performance.

Collaboration & Communication:

 * Work closely with product managers, data scientists, and engineering teams to brainstorm, design, and deploy AI/ML solutions, documenting procedures and best practices.

Leadership & Advocacy:

 * Lead efforts in integrating emerging AI tools, mentor junior team members, and communicate progress and challenges to leadership.