Senior Software Engineer, Data

Fathom; Toronto; On-site; CAD 160,000 - 220,000; Full time

Job summary

Ein innovatives Unternehmen sucht einen Senior Software Engineer, Data, um an der Entwicklung von Datenprodukten zu arbeiten, die das Herzstück des Geschäfts bilden. In dieser Rolle werden Sie daran arbeiten, Dateninfrastrukturen zu entwickeln, die medizinische Daten effizient verarbeiten und normalisieren. Wenn Sie über umfangreiche Erfahrung im Aufbau von Datenpipelines und der Skalierung von Rechenclustern verfügen, könnte diese Position genau das Richtige für Sie sein. Sie werden in einem dynamischen Team arbeiten, das sich leidenschaftlich dafür einsetzt, maschinelles Lernen in der Gesundheitsbranche zu fördern und zu unterstützen. Dies ist eine großartige Gelegenheit, um Ihre Fähigkeiten in einem bedeutenden Bereich weiterzuentwickeln.

Qualifications

• Mindestens 5 Jahre Erfahrung in der Softwareentwicklung in einem Produktionsumfeld.
• Erfahrung im Aufbau von Datenpipelines aus verschiedenen Quellen.

Responsibilities

• Entwicklung der Dateninfrastruktur zur Verarbeitung medizinischer Daten.
• Erstellung von Schnittstellen zur Datenvisualisierung und Analyse.

Skills

Entwicklungserfahrung

Datenpipelines

Datenverarbeitung

Hadoop

Spark

NLP-Toolkits

Datenanalyse

Tools

AWS

Amazon Turk

DeepDive

Job description

Fathom is on a mission to use AI to understand and structure the world’s medical data, starting by making sense of the terabytes of clinician notes contained within the electronic health records of the world’s largest health systems. Our deep learning engine automates the translation of patient records into the billing codes used for healthcare provider reimbursement, a process today that costs hospitals in the US $15B+ annually and tens of billions more in errors and denied claims. We are a venture-backed company that completed a Series B round of financing for $46M in late 2022.

We are looking for a Senior Software Engineer, Data to work on data products that drive the core of our business. We want to work with teammates in the Toronto area, who are excited about learning how to build and support machine learning pipelines that scale not just computationally, but in ways that are flexible, iterative, and geared for collaboration. If you are a data expert able to unify data, and build systems that scale from both an operational and an organizational perspective, Fathom is an interesting opportunity to explore.

Your role and responsibilities will include:

• Developing data infrastructure to ingest, sanitize and normalize a broad range of medical data, such as electronics health records, journals, established medical ontologies, crowd-sourced labelling and other human inputs
• Building performant and expressive interfaces to the data
• Creating infrastructure to help us not only scale up data ingest, but large-scale cloud-based machine learning

We are looking for a teammate with:

• 5+ years of development experience in a company/production setting
• Experience building data pipelines from disparate sources
• Hands-on experience building and scaling up compute clusters
• A solid understanding of databases and large-scale data processing frameworks like Hadoop or Spark and the ability to evaluate which tools to use on the job
• A unique combination of creative and analytic skills apt of designing a system capable of pulling together, training, and testing dozens of data sources under a unified ontology

Bonus points if you have:

• Know-how of developing systems to do or support machine learning, including experience working with NLP toolkits like Stanford CoreNLP, OpenNLP, and/or Python’s NLTK
• Expertise with wrangling healthcare data and/or HIPAA
• Experience with managing large-scale data labelling and acquisition, through tools such as through Amazon Turk or DeepDive

Salary range:

• $160 000 - $220 000 CAD

About the company

What if you had a coder who could code millions of charts per day at unparalleled accuracy and cost? Fathom uses AI to automatically code charts straight to billing.