Staff Machine Learning Software Engineer

Remote Mountain View, CA
USD 90k - 500k
Machine Learning Python TensorFlow PyTorch

At Groq, we radically simplify compute to accelerate workloads in artificial intelligence, machine learning, and high-performance computing.

Why join Groq? You want to be a part of something groundbreaking, where every day you can see the impact of your work on Groq’s technology and customer solutions.  As a Groqstar, you will join a talent-rich group of problem solvers and doers; in a culture that focuses on team, growth, innovation, and creativity. Simply put, at Groq, we defy gravity. 

We are changing as the world changes and have evolved into a Geo-agnostic company meaning you work where you are. Exceptional candidates will thrive in asynchronous partnerships and remote collaboration methods. Some roles may require being located near our primary sites, which will be indicated in the job description. We offer a competitive salary & benefits package, numerous quality-of-life perks such as a home office stipend, flexible learning allowance, optional professional coaching and a schedule of fun team activities. 

Are you ready to join our crew and help us reimagine machine learning and AI at scale? 

If so, we look forward to connecting with you!

Staff Machine Learning Software Engineer

About this role: 

We are looking for exceptional machine learning developers/ engineers/researchers with experience developing machine learning models. As a part of the ML Systems team at Groq, you will be working closely with Groq's sales, applications and engineering teams to develop and optimize ML models and systems for our hardware as well as contribute to original research in the field. 

Responsibilities & opportunities in this role:

  • Develop kernels and models (both customer and public domain models) for Groq hardware using low level proprietary frameworks as well as popular higher level machine learning frameworks. The models will span domains from machine learning (computer vision, natural language processing, recommendation engines, reinforcement learning) to high performance computing (linear algebra) 
  • Optimize models for Groq’s hardware by exploiting proprietary hardware features
  • Performance analysis of models on Groq hardware
  • Performance analysis of large scale systems built using Groq hardware
  • Performance analysis of models on competitor hardware/systems
  • Contribute to driving features into Groq’s hardware based on model optimizations/insight  
  • Publish research papers related to ML model optimizations, hardware, in top tier ML conferences.

Ideal candidates have/are:

  • Analytical background with the ability to quickly understand complex hardware technologies, understand tradeoffs and build systems using them.
  • ML (Neural Networks) and math fundamentals expertise, with some experience/deep understanding in one or more of the following areas:
    • Computer vision
    • Natural Language Processing
    • Recommendation engines
    • Reinforcement Learning
    • Linear algebra
  • Experience with a subset of the following:
    • Python and C/C++
    • TensorFlow, Pytorch, Caffe or other ML Frameworks
    • HW accelerator programming languages such as CUDA, MKLDNN
    • Programming experience on other accelerators such as FPGAs, or DSPs from evaluation to production.
  • Understanding of processor architectures and distributed systems and their implications on ML model performance
  • Strong writer and public speaker; operate with integrity and drive transparency, openness, and effective communication. 

Qualifications for this role:

  • BS in CS, CE/EE, Math, or Physics or equivalent work experience.
  • 2 to 10 years of software and machine learning experience 
  • Recent PhD computer science, math or engineering graduates with ML experience will also be considered.
  • Publication record in ML conferences (ICML, NeurIPS, ICLR, CVPR) is a plus
  • Excellent leadership, mentoring and cross-functional collaborative and influencing skills.
  • Effective communication & presentation skills
  • Able to work in a very dynamic start-up environment

Attributes of a Groqstar:

  • Humility - Egos are checked at the door
  • Collaborative and Team Savvy - We make up the smartest person in the room together
  • Growth and Giver Mindset - Learn it all versus know it all, we share knowledge generously
  • Curious and Innovative - Take a creative approach to projects, problems, and design
  • Passion, grit, and boldness - no limit thinking; fueling informed risk taking

At Groq:

Our goal is to hire and promote an exceptional workforce as diverse as the global populations we serve. Groq is an equal opportunity employer committed to diversity, inclusion, and belonging in all aspects of our organization. We value and celebrate diversity in thought, beliefs, talent, expression, and backgrounds. We know that our individual differences make us better. Come join us! #LI-Remote


The national pay range for our technical roles are $100,000-$500,000. The national pay range for our non-technical roles are $90,000-$470,000. Individual compensation will be commensurate with the candidate’s experience aligned with Groq’s internal leveling guidelines and benchmarks.


Groq is an Equal Opportunity Employer that is committed to inclusion and diversity. Qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, disability or protected veteran status.  We also take affirmative action to offer employment opportunities to minorities, women, individuals with disabilities, and protected veterans.

Groq is committed to working with qualified individuals with physical or mental disabilities. Applicants who would like to contact us regarding the accessibility of our website or who need special assistance or a reasonable accommodation for any part of the application or hiring process may contact us at:  This contact information is for accommodation requests only.  Evaluation of requests for reasonable accommodations will be determined on a case-by-case basis.