Anomaly is enabling fast, accurate healthcare payments—reducing healthcare costs and complexity.
Over $300B is lost every year to avoidable denials and payment errors, impacting the affordability of healthcare. We’re partnering with the largest healthcare companies, including providers and payers, to build novel technology that reduces friction in healthcare payments. Smart Response uses AI to predict and prevent payment errors before they occur. This solution is powered by our claim prediction engine, which analyzes billions of transactions to predict claim denials and reasons with over 97% precision. Instant Pay (coming soon) will further streamline payments by enabling providers to immediately get paid at claim submission. Founded in 2020, we recently announced $17M in Series A and seed funding led by Redesign Health, RRE, and other top venture investors. Our fast-growing team brings together top engineers and healthcare experts committed to making healthcare more efficient and affordable.
Our engineering blog includes more info on the problem we're solving and large datasets we work with.
Staff Software Engineer
As a Staff Software Engineer you will play a key role in building the core platform that powers Anomaly's predictive products to make healthcare payments more efficient. This is a back-end systems role with room to focus on one or more areas including: building high volume, low latency APIs; creating robust data and ML pipelines; and building tools to accelerate our fundamental research and development.
In this role, you will closely collaborate with other engineers, PhD-level data scientists, and payments domain experts as we build products to streamline healthcare payments and reduce cost and complexity.
You will report directly to the CTO. The role is open to fully remote candidates who are US-based, with an expectation of occasional travel to our NYC headquarters.
What you'll do:
- Build integrations to healthcare and financial systems
- Automate end-to-end ingestion and data delivery pipelines with an eye towards observability and data quality
- Create robust integrations with partner health care systems by thinking through and intentionally addressing failure modes upfront
- Build production-level machine learning products
- Produce features in both our PySpark offline training pipeline and online serving API
- Help the team train and test our models on billions of data points in reasonable wall clock and CPU time
- Continuously measure model performance drift and automate the mitigation strategies
- Maintain our tight API latency SLAs while executing dozens of predictions per HTTP request
- Expect the unexpected and build system designed to deal with bad input and unreliable networks
- Build well tested, reusable tools to accelerate our research and development efforts
What you'll need:
- 6+ years of professional software engineering experience
- Prior experience as technical leader
- Ability to write idiomatic, testable and maintainable code in Python, Java or Scala
- Hacker mindset
- Nice To Have
- Expertise building reliable data and ML pipelines with SQL, Spark or Airflow
- Expertise building online ML products
- Expertise building high volume, low latency APIs
Apply here, or reach out to email@example.com with questions.
Life at Anomaly
Headquartered in NYC with a strong remote team, Anomaly brings together a diverse group deeply committed to our mission to bring streamline healthcare. Data scientists, engineers, clinicians, and more work together to realize a future where healthcare payments flow with precision. We live by our values, which help us accomplish more and create a workplace we love.
- Move with urgency: Our customers needed our product yesterday. We move fast—but not too fast—to bring our solution to market
- Be willing to sit on the floor: No one is too senior to jump in and get the job done
- Think 10x: We celebrate incremental improvements, but we always look for the step-function win
- Seek feedback at 30%: We ask for feedback uncomfortably early, and offer it proactively (and respectfully) to others
- Default to open: We internally share everything we can, creating a culture of trust and empowerment
- Be kind: We take our work seriously, but we’re never too busy to be kind