AI Developer

Howard Hughes Medical Institute•Headquarters, KY

10d•$146,947 - $238,789•Hybrid

About The Position

The EverydayAI Accelerator at HHMI is dedicated to integrating generative AI into the daily operations of HHMI's administrative and operational functions. AI Developers are responsible for designing and building AI systems that are deployed within delivery teams, working collaboratively with engineers, architects, and business partners. This role emphasizes enterprise product engineering, requiring practical experience in systems that are versioned, tested, deployed via CI/CD, and operated under Service Level Agreements (SLAs), with a strong foundation in machine learning and deep learning. The work is hands-on and end-to-end, encompassing the co-design, building, deployment, instrumentation, and production operation of AI systems, including troubleshooting and issue resolution. This role is crucial for transforming HHMI's operations by shipping functional AI systems that address real business problems.

Requirements

Enterprise product engineering background with proven experience building and shipping production enterprise systems.
Deep understanding of production-grade engineering practices: versioning, testing, code review, release management, deprecation, and operational ownership.
Real machine learning and deep learning depth, with hands-on experience building and shipping systems using classical ML and modern deep learning.
Solid grounding in relevant ML/DL terminology, mathematics, and evaluation methods.
Strong proficiency in Java and Python.
At least two years of production ML or DL experience beyond generative AI.
Familiarity with agentic AI frameworks such as LangGraph or Strands.
Ability to articulate trade-offs across different ML techniques (cost, latency, reliability, explainability) and select the right approach.
Experience designing evaluation methodology for production ML systems, including metrics, offline/online frameworks, A/B tests, and statistical inference.
Fluency with infrastructure as code (e.g., Terraform), container orchestration, CI/CD, and deployment automation.
Comfort designing systems that are cloud-agnostic and can move fluidly across AWS, Azure, and GCP.
Ability to engage with business leaders, align with architects, and write production code.
Bachelor’s degree or equivalent, plus at least six years of hands-on software engineering experience.
At least three years building and shipping production machine learning or deep learning systems.

Nice To Haves

Familiarity with front-end frameworks (React or comparable) for building end-to-end user-facing AI features.
Production experience with vector databases, retrieval systems, or knowledge graphs.
Familiarity with MLOps tooling: model registries, feature stores, training pipeline orchestration.
Prior experience in research, academic, or mission-driven institutional environments.

Responsibilities

Build production AI systems collaboratively within a delivery team alongside engineers, architects, and business partners.
Select the appropriate algorithm for a given problem, considering options like large language models with retrieval, gradient-boosted trees, or logistic regression.
Adhere to and contribute to the team's established reference architectures, shared services, and engineering patterns.
Design and implement evaluation methodologies before system deployment, including metrics for precision, recall, calibration, drift, business outcomes, and A/B tests.
Own the deployment and operations of AI systems, including CI/CD, infrastructure as code, observability, and cost management.
Partner with receiving teams for smooth handoffs of operational systems, including documentation, runbooks, and ownership transition.
Communicate technical trade-offs and decisions effectively to both business stakeholders and engineering peers.