AKASA is hiring a

Senior Software Engineer, Computer Vision

Full-Time
Remote
About AKASA

Our mission is to enable human health.  

AKASA is the leading developer of AI for healthcare operations. Founded in 2019, AKASA has raised $85M in funding and has grown to more than 200 employees.  

Our founding team includes Silicon Valley leaders who have founded or been founding team members of multiple companies with successful exits. Our investors include Andreessen Horowitz, BOND, and Costanoa Ventures, among others. Recognized in 2021 among Top 150 “Most Innovative Digital Health Startups” by CB Insights, Top 75 “Best Places to Work” by Modern Healthcare, Certified as a “Great Place to Work” two years in a row, and ranked #34 of Top 100 Healthcare Companies in 2021 by The Healthcare Technology Report. Learn more at www.AKASA.com.  

We are building the future of healthcare with AI.  Everyone is welcome — as an inclusive workplace, our employees are comfortable bringing their authentic selves to work.  

Join us.  

About the Role
 
As a remote Senior Computer Vision Software Engineer on our Machine Learning team, you will develop state of the art machine learning models and services that leverage large proprietary medical and clinical datasets. Unlike most companies where machine learning is bolted on to existing software systems, AKASA was started with machine learning at the core of all of our systems, and so as a machine learning engineer your work will directly impact our customers immediately.
 
AKASA is based in South San Francisco. As a company, we embraced remote work before COVID-19. We consider ourselves experts in working collaboratively wherever our team members happen to reside within the US.

What You'll Do

  • You will focus on using your expertise in computer vision to:
  • Participate in developing state-of-art machine learning solutions to address large-scale healthcare problems
  • Write production-ready software with fast and efficient algorithms
  • Join a high performing team and contribute to AKASA’s drive to be on the cutting edge of technology through publications and patents

Skills & Qualifications

  • 4+ years work experience on computer vision systems
  • BS/MS/PhD in Computer Science or related fields and/or relevant work experience
  • Ability to write robust code in Python
  • Experience solving problems using Machine Learning frameworks and libraries such as OpenCV, PyTorch, or equivalent tools
  • Experience working with large language models (LLM)
  • Ability to solve complex problems across different systems
  • Desire to go above and beyond what's asked for
  • Publications in top computer vision journals is a plus

What We Offer

  • Unlimited paid time off (PTO)
  • Expansive coverage for health, dental, and vision
  • Employer contribution to Health Savings Accounts (HSA)
  • Generous parent leave policy
  • Full employee coverage for life insurance
  • Company-paid holidays
  • 401(K) plan

Compensation

  • Based on market data and other factors, the salary range for this position is $175,000-$230,000 + Equity. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

We’re committed to doing the best work of our lives, together. Come see if we're the right team for you.

AKASA is a proud equal opportunity employer and we believe that a diverse and inclusive workforce is an imperative. We welcome people of different backgrounds, genders, races, ethnicities, abilities, sexual orientations, and perspectives, just to name a few. We do not discriminate based upon any protected class and we encourage candidates of all identities and backgrounds to apply. AKASA considers qualified applicants regardless of criminal histories in accordance with the San Francisco Fair Chance Ordinance.

AKASA is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].

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