Machine Learning Researcher, Komodo Health
$230-270k
Comprehensive patient journey data
Be an early applicant
Comprehensive patient journey data
501-1000 employees
Be an early applicant
$230-270k
501-1000 employees
Company mission
To reduce the burden of disease.
Role
Who you are
- This is not just an academic role; we are looking for a researcher with a "Platform Builder" mindset who is eager to move beyond experimental code to create durable, versioned models that enhance our data infrastructure and AI capabilities
- Advanced Research Background: Deep expertise in Machine Learning, Statistics, or a related quantitative field with a strong track record of "solution-oriented" research
- Python Mastery: Expert-level proficiency in Python and the broader ML stack for custom API connector development and data reconciliation
- Proficiency in Quantitative Reasoning: An ability to understand complex data nuances and use that knowledge to scope out technical solutions for platform-level challenges
- Curiosity & Initiative: A proactive drive to explore new AI tools and workflows, with a commitment to sharing AI-enhanced automations with the broader team
- Collaborative Communicator: Ability to translate complex model limitations and research findings into actionable insights for both technical and non-technical stakeholders
What the job involves
- As an ML Researcher, you will be at the core of developing the proprietary models that power our Healthcare Map and the Marmot generative AI platform. You will bridge the gap between theoretical machine learning and practical, high-scale application, conducting original research to improve our ability to link, de-duplicate, and normalize complex longitudinal patient data
- Execute Original Research: Design and test new ML architectures to solve challenges in data integration, interoperability, and predictive modeling across diverse datasets
- Collaborate on Model Architecture: Partner closely with Product and Engineering to ensure research efforts align with revenue-critical IAM and data-orchestration goals
- Adopt a Reflexive AI Mindset: Use AI as a natural part of your own research process to iterate faster and deliver higher-quality code and documentation
- Ensure Technical Excellence: Maintain the highest standards of technical accuracy, ensuring that all models are robust, scalable, and built for a high-performance SaaS environment
- Automate Data Reconciliation: Build API-driven reconciliation jobs and data normalization scripts to ensure a unified and consistent view across our data estate
- Looking back on your first 12 months at Komodo Health, you will have accomplished..
- Advanced Internal Data Fidelity: Developed and deployed novel ML models that improve the accuracy of patient-level data linking and entity resolution across disparate sources
- Enhanced Generative Capabilities: Contributed key architectural improvements to the Marmot LLM infrastructure, specifically focusing on reducing manual "drift" and accelerating feedback loops
- Optimized Non-Human Identity Governance: Established protocols for governing AI agents and automation entities within our platforms, ensuring least-privilege access and "Compliance-by-Design"
- Bridged Research and Production: Successfully collaborated with advanced modeling and engineering groups to transition research prototypes into production-ready platform features
Our take
Komodo Health's Healthcare Map tracks the healthcare journeys of over 330M patients, giving providers, pharma companies, and researchers the tools to identify opportunities for care, track treatment outcomes, and uncover patterns in patient and drug behavior.
The platform tackles the fragmentation of healthcare, helping organizations act on reliable, real-world intelligence instead of incomplete or siloed date.
The company's been making some big moves recently too, with the launch of its healthcare-native AI engine Marmot™ and partnering with Anervea.ai to bring Ai directly into clinical development. With these innovations and strategic partnerships, Komodo is in a really good spot to lead the future of AI-driven healthcare intelligence.
Freddie
Company Specialist at Welcome to the Jungle
Insights
-4% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Dec 2022
$200m
LATE VC
Mar 2021
$220m
SERIES E
Total funding: $514m
Company HQ
Midtown Manhattan, New York, NY
Leadership
Arif Nathoo
(Co-Founder & CEO)Studied Medicine at Harvard. Former Associate Principal at McKinsey & Co for 7 years. Became a Senior Advisor for 2 years.
Web Sun
(Co-Founder & President)Previously worked in Sales and Marketing at Merck&Co. VP of Business Development at Campbell Alliance.
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