Engineering Manager, ML Vision and Multimodality

SambaNova Systems

SambaNova Systems

Software Engineering, Other Engineering, Data Science
United States · Remote
Posted on Saturday, August 26, 2023

The third era of AI has arrived, powered by Generative AI. Generative AI is achieving step-function increases in scale, versatility, and accuracy compared to legacy AI technologies, presenting an opportunity for organizations to fundamentally transform their business and operations.

SambaNova Suite™ is enabling organizations and enterprises to achieve the transformative promise of these new AI technologies with a fully integrated hardware-software system that delivers innovation across the full AI stack, including the most accurate generative AI models, optimized for enterprise and government. This creates the AI backbone for the next 10 years and beyond.

Job Summary

We are looking for a world-class engineering leader to manage a team of talented Machine Learning engineers and researchers driving the development & innovation of our vision technology. One must thrive in a fast-paced environment, where you'll work closely with cross-functional teams to optimize performance and drive velocity. Leveraging cutting-edge techniques, you will play a vital role in our overall success in deploying state of the art AI capabilities all around the globe.


  • Lead and manage a high-performing team of machine learning engineers in a fast-paced environment, providing technical guidance, mentorship, and support to drive their professional growth and development.
  • Oversee the rapid development and implementation of machine learning models, leveraging advanced algorithms and techniques to optimize performance.
  • Collaborate closely with cross-functional teams, including product managers, software engineers, and data engineers, to deliver data-driven insights and recommendations that enhance our solutions in an agile environment.
  • Stay at the forefront of industry trends, emerging technologies, and best practices in machine learning, vision and MLOps. Apply this knowledge to drive innovation, meet tight deadlines, and maintain a competitive edge.
  • Establish and maintain strong relationships with stakeholders, providing clear communication of technical concepts and findings to both technical and non-technical audiences.

Skills & Qualifications

  • Master's or PhD in a quantitative field such as Data Science, Computer Science, Statistics, or a related discipline.
  • 8+ years of experience in Machine Learning, with a focus in vision.
  • 2+ years proven success in leading and managing a team of 5 or more ML engineers, driving their professional growth and delivering impactful projects.
  • Strong expertise in machine learning algorithms, and data analysis techniques.
  • Proficiency in Python, with hands-on experience using machine learning libraries and frameworks such as Pytorch, Tensorflow, or JAX.
  • Strong communication and collaboration skills, with the ability to effectively convey technical concepts to both technical and non-technical stakeholders in a fast-paced context.
  • Experience and familiarity with production ML environments, including model release, evaluation and monitoring.

Preferred Qualifications

  • Track record of published ML papers and/or blogs.
  • Track record of engagement with open-source ML community.
  • Experience with Vision applications in AI for Science, Oil and Gas, or medical imaging.
  • Experience with Vision and Multi-modal foundation models such as Stable Diffusion, ViT and CLIP.
  • Experience with performance optimization of ML models.
  • 2+ years of experience in a startup environment.

Annual Salary Range and Level

The base salary for this position ranges from $190,000/year up to $215,000/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.

Benefits Summary

SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 80% premium coverage for dependents and offer a Health Savings Account(HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans in addition to Flexible Spending Account(FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits includes a full subscription to Headspace, access to One Medical, counseling services with an Employee Assistance Program, and much more.

Submission Guidelines

Please note that in order to be considered an applicant for any position at SambaNova Systems you must submit an application form for each position for which you believe you are qualified.

If you are a new, recent (within the last two years), or upcoming college graduate and are interested in opportunities with SambaNova Systems, please apply through our University job listings.

EEO Policy

SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Customers turn to SambaNova to quickly deploy state-of-the-art AI capabilities to meet the demands of the AI-enabled world. Our purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. We enable customers to unlock the valuable business insights trapped in their data. Our flagship offering, SambaNova Suite™, provides the most accurate generative AI models, optimized for enterprise and government organizations, deployed on-premises or in the cloud, and adapted with an organization’s data for greater accuracy