Join AKASA as a Sr. Machine Learning Engineer to develop state-of-the-art ML solutions that empower healthcare professionals.
Posted by employer 5 months ago
First seen on Joblaze 1 month ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
Our mission is to empower healthcare professionals with tools that amplify their capabilities, making them faster, more comprehensive, and more effective.
Requirements
Not disclosed in this posting: visa sponsorship.
Benefits
Joblaze summary
In the role of Sr. Machine Learning Engineer at AKASA, the individual will engage in developing and deploying machine learning solutions that directly impact healthcare operations. Key skills include expertise in large language models, proficiency in programming languages like Python and C++, and experience with cloud platforms and ML frameworks. This position is ideal for seasoned professionals with a strong background in machine learning and data engineering, particularly those who thrive in a collaborative environment with a focus on innovative healthcare applications.
Joblaze insights
Quick facts
From the original posting
About AKASA
About the Role
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
Skills & Qualifications
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
What We Offer
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
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.
Standard company text repeated across AKASA's postings is omitted here.