Staff Platform Engineer | Expansion
MachinifyRemote - US$220k – $245kPosted 21 March 2026
Job Description
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
We're looking for a skilled Staff Platform Engineer to join our foundational engineering team! This position is critical to Machinify’s AI platform expansion initiatives. In this role, you will leverage your deep programming expertise to design and build scalable, reliable backend systems. The ideal candidate has strong experience in application system architecture and design, as well as proven leadership skills, whether in serving as a tech lead or guiding complex and extensible platform projects.
You'll join a highly visible engineering team trusted to solve complex, high-stakes problems using the best of AI, data and software systems. Our team partners cross-functionally to influence product direction, work directly with customers, and own systems positively impacting millions of lives—where your technical decisions truly matter!
What You’ll Do
Owning platform extensibility and scalability which is used as the foundation for web services, data services and Machinify’s AI systems
Develop and maintain scalable and distributed systems to keep them extensible, scalable, and secure
Design, develop, and maintain robust Scala and Java based systems
Implement and manage distributed PostgreSQL database systems to ensure optimal performance and data integrity
Collaborate cross-functionally with other teams that define, design, and ship new features, with the understanding of how our systems must change to support AI platform expansion for future customer-focused deliverables
Design and implement complex frameworks for applying machine learning techniques to large data volumes
Deliver reliable and good-performing software
Troubleshoot and resolve software and database issues in a timely manner.
Participate in code reviews to maintain high code quality.
Continuously explore new technologies and integrate them into projects to improve system performance and scalability.
What You Bring
10+ years of strong depth of knowledge in distributed systems, microservices architecture, and database technologies.
Ability to gain trust and influence to adopt a specific technical direction or architectural change without having direct managerial authority across stakeholders
Strong working experience with Scala or Java (additional experience with python and Rust is a plus)
Experience with SQL, database schema design, and relational database development/administration (PostgreSQL preferred)
Knowledge of distributed computing frameworks (e.g. Apache Spark, Apache Kafka,).
Skilled in reading the source for complex software systems, understanding them, and improving them
Proven skills to design, build, and optimize software systems that manage, process, and analyze massive datasets (Big Data) (Spark experience ideal)
A strong CS foundation in data structures and asynchronous programming
BS or MS in Computer Science (or equivalent experience)
What We Offer
Work from anywhere in the US! Machinify is digital-first.
Top Medical/Dental/Vision offerings
FSA/HSA
Tuition reimbursement
Competitive salary, 401(k) with company match
Unlimited PTO
Additional health and wellness benefits and perks
Flexible and trusting environment where you’ll feel empowered to do your best work
The salary for this position is based on an array of factors unique to each candidate: Such as years and depth of experience, set skills, certifications, etc. W ... (truncated, view full listing at source)
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