Applied AI Engineer

Summary

Join Komodo Health, a company dedicated to reducing the global burden of disease through data-driven solutions, and become an integral part of their AI team, Labs@Komodo. This role focuses on building AI-native products and tools to enhance both market-facing healthcare solutions and internal workflows. You will rapidly prototype and launch AI agents to optimize engineering processes and streamline operations. The position requires a strong background in AI/ML, proficiency in Python and relevant toolkits, and experience with LLMs and agent orchestration. You will be responsible for transitioning prototypes into scalable systems and exploring emerging AI models and toolchains. Success in this role involves delivering production-ready AI products, shaping Komodo's AI strategy, and mentoring others in AI problem-solving.

Requirements

• A track record of building AI-powered systems that move rapidly from prototype to production
• Strong proficiency with LLMs, prompt engineering, agent orchestration (LangChain, CrewAI, etc.), and multi-agent systems
• Fluency in Python and hands-on experience with ML toolkits (e.g., PyTorch, HuggingFace, scikit-learn)
• Deep understanding of AI/ML fundamentals, with applied experience solving real-world problems using an AI-first approach
• Comfort building quick demos, debugging AI behavior, and translating ideas into functional agent workflows or model pipelines
• The engineering discipline to test, refactor, and scale systems with reliability, observability, and maintainability in mind
• Contributions to open-source AI projects or participation in GenAI hackathons, agentic challenges, or public tooling communities
• A system-builder mindset: equally motivated to craft internal AI tools and deliver customer-facing solutions
• Strong communication and collaboration skills—able to work across technical teams and influence non-technical stakeholders
• Degree in Computer Science, Machine Learning, or a related field—or equivalent hands-on experience that speaks for itself
• This role is AI-native by design—you are expected to apply GenAI, agent-based workflows, and cutting-edge models as your default problem-solving toolkit
• Automating your own workflows using tools like Gemini, GPT-4, Claude, etc. and internal agents
• Driving experimentation and adoption of new AI techniques within your team and across Komodo
• Thinking in systems: understanding how prompt chains, orchestration logic, and fine-tuning loops can be built into scalable solutions

Responsibilities

• Designing and building AI-native products and tools that address both market-facing healthcare challenges and internal workflow optimization
• Driving experimentation by developing working demos and iterating based on fast feedback from users and data
• Prototyping rapidly using GenAI models, chaining techniques, and orchestration frameworks to test ideas in days—not quarters
• Partnering with engineers, product teams, and operational stakeholders to identify AI leverage points across workflows
• Researching and applying state-of-the-art AI techniques—LLMs, agent-based systems, generative models, etc.—to both structured and unstructured datasets
• Building internal systems that enhance developer productivity: think agents for code review, documentation generation, or dynamic prompt libraries
• Architecting scalable ML infrastructure and maintaining high standards of reproducibility, explainability, and observability
• Transitioning successful prototypes into robust, scalable systems—with attention to reproducibility, observability, and maintainability
• Continuously exploring emerging models and toolchains (e.g., Gemini, GPT-4, Claude, open-source agents) and evaluating them through practical application

Preferred Qualifications

• Ph.D. in a relevant AI/ML field
• Experience with specific healthcare data modalities (e.g., claims, EHR, genomic data)
• Publications in top-tier AI/ML conferences or journals
• Experience with distributed computing frameworks (e.g., Spark, Dask) for large-scale AI applications
• Experience with MLOps best practices and tools for model lifecycle management

Benefits

• Comprehensive health, dental, and vision insurance
• Flexible time off and holidays
• 401(k) with company match
• Disability insurance and life insurance
• Leaves of absence in accordance with applicable state and local laws and regulations and company policy
• Performance-based bonuses
• Equity awards
• Medical, dental and vision coverage
• 401k Retirement Plan
• Prepaid legal assistance
• Paid time off for vacation, sickness, holiday, and bereavement
• 100% company-paid life insurance and long-term disability insurance