AI and Machine Learning Engineer I Graduate

Hewlett Packard Enterprise•San Jose, CA

8d•$92,700 - $187,800•Onsite

About The Position

Develops and programs integrated software algorithms to structure, analyze and leverage structured and unstructured data in product and systems applications. Can work with large scale computing frameworks, data analysis systems, and modeling environments. Uses machine learning and statistical modeling techniques to improve product/system performance, data management, quality, and accuracy. Formulates descriptive, diagnostic, predictive and prescriptive insights/algorithms and translates technical specifications into code. Applies, optimizes and scales deep learning technologies and algorithms to give computers the capability to visualize, learn and respond to complex situations. Documents procedures for installation and maintenance, completes programming, performs testing and debugging, defines and monitors performance metrics. Contributes to the success of HPE by translating customer requirements and industry trends into AI/ML products, solutions, and systems improvement projects.

Requirements

Bachelor's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
Typically, 0-2 years’ experience.
Proficiency in programming languages such as Python, R, or Java is required.
Experience with SQL and databases and data visualization tools like Matplotlib or Tableau is required.

Nice To Haves

Master’s degree is desirable.
Knowledge of libraries and frameworks commonly used in AI and machine learning, such as TensorFlow, PyTorch, or scikit-learn, is highly beneficial.
A solid understanding of statistics, probability, linear algebra, calculus, and optimization methods is crucial for building and evaluating machine learning models.
In-depth knowledge of machine learning algorithms, techniques, and concepts is essential. This includes supervised and unsupervised learning, deep learning, neural networks, reinforcement learning, and natural language processing.
Proficiency in working with large datasets, data pre-processing, data cleaning, and exploratory data analysis is necessary.

Responsibilities

Partner with cross‑functional teams to identify opportunities for data‑driven improvements and translate business needs into technical solutions.
Ingest and integrate data from structured and unstructured enterprise systems, including Databricks and IT data platforms.
Design and build curated data models and analytical layers to support reporting and downstream analytics.
Develop, optimize, and maintain pipelines using SQL, Python, and orchestration tools.
Clean, transform, and prepare datasets for reliable consumption across the business.
Ensure data quality and observability across pipelines, workflows, and storage layers.
Integrate new data sources, APIs, and event streams into the platform.
Create clear data documentation and communicate technical concepts to non‑technical stakeholders.
Stay current with modern data engineering tools, cloud technologies, and best practices.