Client AI Hardware research intern

Job Description

This project will involve research for client AI hardware. The candidate is expected to work closely with the Intel Client AI HW Advanced Architecture team to drive VPU architecture, design, analysis, and optimization activities. In this position, you will function as a technical leader on the Advanced Architecture team, and you will impact and influence the Artificial Intelligence product roadmap and development based on profound comprehension of Artificial Intelligence and Deep Learning algorithms, Deep Learning customer requirements, and Deep Learning software frameworks. Your contribution will immediately impact current and future products on Intel's Client AI product map. Responsibilities may include, but are not limited to 

• Develop Augmented Intelligence (AI + domain expertise) based solutions to drive holistic design optimization decisions during the architecture and design phases of client products.
• Identify, evaluate, and develop RTL prototypes of key architectural features that improve the PPA of the VPU IP. 
• Support cross-divisional collaboration between VPU/IL teams on modeling, validation, hardware-software co-design, and backend activities to develop and deliver innovative hardware architectures for the next-generation neural compute IP. 
• Collaborate with the Workload and Algorithm teams to analyze next-generation requirements and opportunities, influence and guide research and academic collaboration in the space of Artificial and Deep Learning.
• Patent and Publish key innovative ideas (An important aspect of the work and necessary for protecting our Intel's IP).

A successful candidate will demonstrate: 

• Strong problem-solving skills.
• Excellent verbal and written communication skills. 
• Must be a team player and flexible to adapt to a dynamic work environment with rapidly changing priorities. 
• Self-motivated and proactive with an independent thinking mindset as an architect. 
• Candidate also bears the responsibility to define and articulate methodology and work collectively with team to deliver the expected results.  

In this position, you will gain invaluable experience, which will allow growth and expanded opportunities within this business group, as well as future possible opportunities with other business groups within Intel.

This assignment is for the Fall semester.

This is an internship position and compensation will be given accordingly.

Qualifications

You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Experience would be obtained through a combination of prior education level classes, and current level school classes, projects, research, and relevant previous job and/or internship experience.

Minimum Qualifications:

• The candidate must be pursuing a Master's degree or PhD Deree in Computer Science, Electrical Engineering or any STEM related field
• Candidate must have at least one semester remaining at school, after completion of the internship.

Preferred Qualifications:

• Experience in Digital Logic Design / RTL Design using System Verilog and/or Verilog.
• Experience working in the AI/DL domain.
• Experience in AI model architectures, layer types, ops, activation functions.
• In-depth experience with AI framework, AI models, and basic neural computing operations.
• Experience with various techniques to optimize models for compute/perf (quantization, compression, sparsity, pruning, low precision).
• Experience in computer architecture concepts such as pipelining, caching, parallel computing with SIMD/VLIW, multi-core/multi-threading, data precision, memory hierarchy.
• Experience with data precision, floating point vs. fixed point computing trade-offs.
• Hands on experience working with Git (PR/MR workflows with GitHub or any Gitlab repos).
• Any hands-on work experience of working with few of the modern DL topologies (ResNet, mBERT, MobileNet, GPT, BERT, LLMs).
• Experience in mapping between Neural Networks architectures and hardware accelerated inference.

Inside this Business Group

Intel Labs is the company's world-class, industry leading research organization, responsible for driving Intel's technology pipeline and creating new opportunities. The mission of Intel Labs is to deliver breakthrough technologies to fuel Intel's growth. This includes identifying and exploring compelling new technologies and high risk opportunities ahead of business unit investment and demonstrating first-to-market technologies and innovative new usages for computing technology. Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units.

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation. Find more information about all of our Amazing Benefits here.



Annual Salary Range for jobs which could be performed in US, Colorado, New York, Washington, Washington DC, California: $63,000.00-$166,000.00
*Salary range dependent on a number of factors including location and experience

Working Model

This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances the work model may change to accommodate business needs.