Senior Applied Machine Learning Engineer
Role details
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Tech stack
Job description
This role has been designed as 'Hybrid' with an expectation 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.
Requirements
- 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).
Benefits & conditions
"The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level.
- United States of America: Annual Salary USD 144,000 - 273,000 in Colorado // 155,500 - 315,000 in California // 137,000 - 315,000 in Texas The listed salary range reflects base salary. Variable incentives may also be offered."