AI Developer

## AI Developer Apply locations:
Headquarters time type:
Full time posted on:
Posted Yesterday job requisition :
R-4475

Primary Work Address: 4000 Jones Bridge Road, Chevy Chase, MD, 20815## Current HHMI Employees,  to apply via your Workday account.

HHMI is focused on supporting and moving science forward in a variety of different ways ranging from conducting basic biomedical research, empowering educators, inspiring students, developing the next generation of scientists – even stretching into film and media production.  Our Headquarters is in the greater Washington, DC metro area and is home to over 300 employees with expertise in investments, communications, digital production, biomedical sciences, and everything in between.  

The work housed here supports and augments the groundbreaking research conducted in HHMI labs across the nation.  As HHMI scientists continue to push boundaries in laboratories and classrooms, you can be sure that your contributions while working here are making a difference.

The Everyday AI Accelerator exists to turn generative AI into working, daily reality across HHMI’s administrative and operational functions. AI Developers design and build the AI systems that actually ship, embedded inside delivery teams alongside engineers, architects, and business partners.

The foundation here is enterprise product engineering. This role requires real production experience: systems versioned, tested, deployed through CI/CD, and operated under SLAs, with genuine machine learning and deep learning depth built on top. The work is hands-on and end-to-end, from co-designing and building AI systems to deploying, instrumenting, and operating them in production. When something breaks, this is the person who diagnoses and fixes it.
** Why this role matters
** The Everyday AI Accelerator exists to change how HHMI operates, and that change only happens when AI systems actually ship. HHMI has no shortage of ideas. What it needs are engineers who can turn them into working systems. This role sits at the center of that work: building the production systems every Accelerator project depends on, taking a real business problem, selecting the right approach, writing the code, and operating what gets built.

The work is technical, consequential, and visible across the organization.
** What you will actually do**
* ** Build production AI systems with the team.
** Work inside a delivery team alongside engineers, architects, and business partners to ship production AI. The contribution is collaborative and integrated, not delivered in parallel.
* ** Pick the right algorithm for the problem.
** Treat model selection as a design choice. Sometimes the right answer is a large language model with retrieval; sometimes it is a gradient-boosted tree; sometimes it is a well-featured logistic regression with a clean evaluation. Bring real ML and DL fluency to that choice and defend it.
* ** Build to the team’s patterns.
** Use the reference architectures, shared services, and engineering patterns the team has established. Contribute back when you find a gap, and raise it through the team rather than working around it. Shared patterns are how the team scales.
* ** Evaluate before you ship.
** Design evaluation methodology before the system goes anywhere near production: precision, recall, calibration, drift, business outcome metrics, and A/B tests where they make sense. Measure what matters and operate to it. A handful of promising examples is not evaluation.
* ** Own deployment and operations.
** CI/CD, infrastructure as code, observability, cost. Code does not go over a wall. Production is designed for from the first commit, with enough proximity to the running system to debug it when something goes wrong.
* ** Partner for handoff.
** When the system is ready to move into a vertical, co-build the handoff with the receiving team: documentation, runbooks, on-call posture, and ownership transition. Leave it operable by others, not as a black box tied to one person.
* ** Communicate across the altitude range.
** Translate model and engineering trade-offs for business stakeholders, and explain the same decisions to engineering peers. Both…