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San Francisco, California, United States
Hybrid
Healthcare operations have always depended on people to bridge the gaps that technology couldn't. It relies on complex manual work to carry out critical internal processes, yet most health systems don’t have enough resources to properly automate these tasks, leaving them stuck in outdated, labor-intensive SOPs.
Luminai structures the chaos, automates the manual handoffs, and deploys end-to-end workflows across every system, providing the integrated intelligence layer to improve processes over time. By delegating to autonomous AI systems those mission-critical workflows that previously expended valuable human time, Luminai allows doctors and administrators to do what they do best: Focus on Patients.
We've raised $60M in funding, including our recent Series B led by Peak XV Partners (formerly Sequoia India), with participation from healthcare-focused Define Ventures and continued support from General Catalyst and Y Combinator. We're backed by some of the best investors in Silicon Valley, including Kevin Weil (Chief Product Officer at OpenAI), Arash Ferdowsi (co-founder of Dropbox), Katie Stanton (former VP Global Media, Twitter), and CEOs of companies such as Flexport, Notion, Front, Ramp, and Twitch.
Our team is in-office 4 days a week (Monday - Friday, Wednesday optional WFH) in either San Francisco, California or New York, New York.
As a Software Engineer working on AI systems, you will play a foundational role in research, experimentation and rapid improvement of AI systems towards building a capable, reliable AI automation platform. The platform is used by organizations worldwide to deploy and scale executable AI automations in mission critical production environments. You are expected to have a strong proficiency in fundamentals of software engineering, a willingness to pick new concepts as needed and an ability to drive technical projects in ambitious environments.
Design experiments and test ideas to optimize key internal AI benchmarks
Design and improve evaluation frameworks to accelerate the speed and direction of experimentation
Train, fine-tune, and optimize machine learning models. Perform rigorous evaluation and testing to ensure model accuracy, generalization, and performance.
Collaborate and contribute on the core product development to deliver higher platform capabilities
Set up observability and monitoring systems to safety check model behavior in critical settings
Proven track record of shipping high-quality code in challenging projects
Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field
Solid fundamentals in algorithms, data structures, system design
Attention to detail and a first-principles thinking towards real world deployment of intelligent systems.
Experience shipping ML models to production
Bonus
Previous experience working with distributed computing systems in production
