Job location
South San Francisco, United States of America
Tech stack
Artificial Intelligence
Amazon Web Services (AWS)
Computer Vision
C++
Information Engineering
Python
Machine Learning
Language Modeling
Named Entity Recognition
Open Source Technology
Performance Tuning
TensorFlow
Azure
PyTorch
Large Language Models
Spark
Scikit Learn
Kubernetes
Information Technology
Machine Learning Operations
Data Pipelines
Programming Languages
Job description
The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery., As a Sr. Machine Learning Engineer, you'll report to our Engineering Manager and work with a talented team of PhD Researchers, ML Engineers, and healthcare experts. You will put machine learning into practice, so your code directly affects our customers immediately. You'll work with large proprietary medical and clinical datasets containing structured documents, natural language and images. Our mission is to empower healthcare professionals with tools that amplify their capabilities, making them faster, more comprehensive, and more effective in their roles.
The AKASA office is located in South San Francisco. While we support remote work on a variety of teams, we have a strong Bay Area presence across the company. The local R&D teams come into the office every Wednesday for co-working days, which this role will be expected to attend.
What You'll Do
- Participate in developing state-of-the-art ML solutions to address large-scale healthcare problems
- Own ML services end-to-end, including problem discovery, data pipeline development, inference optimizations, model experimentation, and service deployment
- Help develop pipelines that collect, preprocess, and deliver data with a measurable quality
- Write production-ready software with well-tested and efficient algorithms
- Develop state-of-the-art ML algorithms across large-language models, probabilistic inference and computer vision to solve problems like medical document entity extraction, medical coding and claim outcome prediction
- Help build novel, application-specific ML models - all of our products are built from the ground up with ML at their core, enabling us to deploy our predictions in new and interesting ways
Requirements
- Master's degree in Computer Science or similar
- 5+ years of work experience in machine learning and data engineering
- Experience launching production ML systems from the ground up
- Experience with Large Language Models, including both open source model deployment and agentic workflows; ideal: experience directly training or fine-tuning LLMs
- Proficiency in one or more programming languages such as Python and C++
- Development experience with cloud platforms such as Spark, AWS & k8s
- Knowledge of ML frameworks such as Scikit-Learn, Pytorch & TensorFlow
Benefits & conditions
- Flexible paid time off (PTO)
- Expansive coverage for health, dental, and vision
- Employer contribution to Health Savings Accounts (HSA)
- Generous parental leave policy
- Full employee coverage for life insurance
- Home office stipend
- Cell phone/internet reimbursement
- Company-paid holidays
- 401(K) plan, * 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.
About the company
About AKASA
At AKASA, our mission is to build the future of healthcare with AI. As the leading provider of generative AI solutions for the healthcare revenue cycle, we help health systems comprehensively capture and communicate the full patient clinical journey. By empowering health systems to streamline their operations, they can focus on what matters most - delivering quality patient care. We have raised over $205M in funding from investors such as Andreessen Horowitz, BOND, and Costanoa Ventures.
This is the most exciting time to join AKASA. Revenue bookings for our new AI-native product suite have grown over 20x since launching in 2024. In this time, we have broken our record for the largest deal in company history three times consecutively. This growth is driven by the massive improvement we are generating for our customers across clinical quality and documentation accuracy, both top priority areas for health system leaders.
Our deployments have been recognized nationally as "one of the most comprehensive real-world uses of GenAI in healthcare finance to date" (link). Our customer base represents more than $120B+ in net patient revenue and includes the most innovative health systems in the country, like Cleveland Clinic, Duke, Stanford, and Johns Hopkins.
Some of our recent recognitions include being named one of America's Top Startup Employers 2026 by Forbes, #1 most promising healthcare RCM startup of 2025 by Black Book Market Research, and one of the fastest-growing GenAI startups to watch by AIM Research. Our CEO was ranked among the "Top 50 Healthcare Technology CEOs" by the Healthcare Technology Report, and we have been certified as a "Great Place to Work" for the past 6 years in a row.
We're building on this momentum to redefine what's possible in healthcare. We're looking for exceptional people to help us accelerate that reality., With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
* Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
* Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
* Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
* Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
* Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
* Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute