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About This Role
AI Data Foundation Research Engineer
This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
Job Description:
Role and Responsibilities
Successful candidates will work on development of infrastructures and algorithms to capture, manage, enhance and interpret meta-data and lineage for AI pipelines to enable reproducibility, reuse and optimization of pipelines; search, discovery, selection and usage of relevant high quality data for trustworthy AI outcomes across multiple AI applications; development, evaluation and testing of Foundation AI models for different modalities: Natural Language Processing - NLP, Large Language Models - LLM, Time Series Analysis, Computer Vision, etc., and augmentation of AI models with structured knowledge (i.e., knowledge infused learning)., including data and knowledge context retrieval, filtering, prioritization, advanced reasoning, reasoning trace capture and validation, to improve quality of AI agentic workflows. Successful candidates will also work on development of Agentic OS infrastructures to enable consistent management of context, reasoning, governance policies and guardrails across different agentic harnesses. We are particularly interested in individuals with a background in computer systems, machine learning, deep learning, statistics, generative AI, data management, and big data pipelines, with good understanding of the current state of the art, major trends and opportunities, and a demonstrated track record in innovative research. The ideal candidate can thrive in an applied research environment, balancing significant technical contributions published externally in open source with the hands-on engineering skill to bring such contributions to practice in partnering with our internal software development teams and external partners.
Qualifications and Education Requirements
PhD in Computer Science or related fields with a focus on data engineering and data science, in particular Machine Learning, Deep Learning, and/or data management for AI plus 3 years of relevant industry experience.
Preferred Skills
- Research experience in Generative AI, Deep Learning and Machine Learning
- Experience with advanced AI model architectures: LLMs, Time Series Foundation Models, Diffusion Models, etc.
- Expertise with end-to-end pipelines for AI and Machine Learning and in particular the data layer underlying the pipelines (e.g., DVC, lakeFS, Pachyderm, Common Metadata Framework, Flowcept)
- Experience in AI model development lifecycle, ML/deep learning frameworks and MLOps platforms (e.g. Pytorch/Tensorflow, MLFlow, Kubeflow, Ray)
- Experience with agentic AI platforms (e.g., LangGraph, CrewAI, ADK, Autogen, LlamaIndex, OpenCode, Cloud Code, Academy, etc.)
- Outstanding analytical and problem solving skills
- Strong programming skills in Python with high proficiency in data structures and algorithms
- Proficiency in using coding agents and co-pilots for accelerated code development
- Experience with CI/CD code development
- Experience in containerized development and orchestration tools (e.g. Kubernetes, Ezmeral)
- Experience with knowledge graphs and knowledge infused learning – a plus
- Expertise in research of data and workflow management systems – a plus
- Experience with hybrid AI-HPC workflows (e.g., AI surrogate modeling, computational steering of experiments and/or HPC simulations) – a plus
- Experience in system software performance and scalability optimization – a plus
- Experience with multi-threaded programming, parallel processing, OOD/OOP/distributed programming – a plus
Accessibility
HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.
Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.
What We Can Offer You:
Health & Wellbeing
We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.
Personal & Professional Development
We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.
Unconditional Inclusion
We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.
Let's Stay Connected:
Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.
#unitedstatesJob:
EngineeringJob Level:
TCP_03
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 132,500 - 252,500 in Colorado
The listed salary range reflects base salary. Variable incentives may also be offered. Information about employee benefits offered in the US can be found at https://myhperewards.com/main/new-hire-enrollment.html
The estimated job application period closure is November 30 2026; this timeline is provided for transparency and internal planning purposes. HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.
Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.
HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.
Recruitment Fraud Alert
We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.
All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.
Salary Context
This $132K-$252K range is above the median for Research Engineer roles in our dataset (median: $192K across 50 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Hewlett Packard Enterprise | HPE, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($192K) sits 24% below the category median. Disclosed range: $132K to $252K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Hewlett Packard Enterprise | HPE AI Hiring
Hewlett Packard Enterprise | HPE has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span MD, US, Fort Collins, CO, US, SC, US. Compensation range: $252K - $652K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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