The ML team is responsible for driving innovation in models and algorithms that run on the SambaNova platform. This scope encompasses cutting edge applied research as well as productizing, deploying, and designing deep learning applications.

You will be responsible for:

• Enabling large language and multi-modal models on SambaNova hardware. 
• Staying in touch with state of the art research and providing feedback on technical decisions within the team.
• Maintaining quality software and working on a project end-to-end. You will be expected to run deep learning experiments as well as productize your code for customer deployment.

Basic Qualifications:

• BS+ degree in engineering or science fields
• 2-4 years of hands-on engineering experience in machine learning
• Experience in Python and/or C++
• Experience with one or more deep learning frameworks like TensorFlow, PyTorch, Caffe2, or Theano
• Familiarity with working in a SW engineering team, and using productivity tools such as Git and Docker
• Experience debugging neural network behavior in a research environment. Demonstrating good experimental hygiene and analysis
• Clear communicating your work to subject matter experts, as well as communicating to members outside of the team

Additional Required Qualifications

• A deep theoretical or empirical understanding of deep learning
• Experience building and deploying machine learning models
• Strong analytical and debugging skills

Preferred Qualifications

• Experience working in a high-growth startup
• A team player who demonstrates humility
• Action-oriented with a focus on speed & results
• Ability to thrive in a no-boundaries culture & make an impact on innovation

Annual Salary Range and Level

The base salary for this position ranges from $130,000/year up to $155,000/year. This range is based on role, level, and location and reflects the salary target for new hires in the US. Individual pay within the range will depend on a number of factors, including a candidate’s job-related qualifications, skills, competencies and experience, and location.