Interested in this AI/ML Engineer role at Howard Hughes Medical Institute?
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About This Role
Primary Work Address: 4000 Jones Bridge Road, Chevy Chase, MD, 20815
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 EverydayAI 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 EverydayAI 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 willactually 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 tothe 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 happen regularly and both matter.
What we are looking for
- Enterprise product engineering background.Proven experience building and shipping production enterprise systems, with a deep understanding of what production\-grade engineering looks like at institutional scale: versioning, testing, code review, release management, deprecation, and operational ownership. The AI work builds on that engineering foundation.
- Real machine learning and deep learning depth.Hands\-on experience building and shipping systems using classical ML (gradient boosting, regression, clustering, tree\-based methods, dimensionality reduction) and modern deep learning, with solid grounding in the relevant terminology, mathematics, and evaluation methods. Strong proficiency in Java and Python is required. This role requires at least two years of production ML or DL experience beyond generative AI, as well as familiarity with agentic AI frameworks such as LangGraph or Strands.
- The right algorithm for the problem.Ability to articulate trade\-offs across techniques (cost, latency, reliability, explainability) and select the right approach for the problem, not just the most fashionable one. Model selection is a deliberate, defensible decision.
- Model evaluation discipline.Experience designing evaluation methodology for production ML systems: selecting appropriate metrics, building offline and online evaluation frameworks, running A/B tests, and applying statistical inference to validate results. Evaluation is how the work is verified, not an afterthought.
- Infrastructure as code and automation by default.Fluency with Terraform or comparable IaC tooling, container orchestration, CI/CD, and deployment automation. Production systems are deployed through repeatable, automated processes. Observability, cost control, security, and graceful degradation are design inputs from the start, not items addressed at the end.
- Cloud\-agnostic mindset.Comfort designing systems that are not unnecessarily tied to a single cloud provider. Managed services are used deliberately, with lock\-in documented. Able to move fluidly across AWS, Azure, and GCP.
- Altitude range.Able to engage with business leaders to understand a process, align with architects on patterns, and then write the code that does the work. Moving between those modes is a core part of this role.
Nice to have
- 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.
What this role is not
- A pure prompt\-engineering role. LLMs are a constant part of the work, but the job is full\-stack AI engineering across the broader ML and DL toolkit. Candidates whose AI experience is limited to the last two years of generative AI will find this role is not the right fit.
- A data engineering role. This role partners with the data and integration teams on the data and knowledge layer, but does not own the pipelines that feed it.
- A research role. Following the field and running real experiments is part of the work, but the primary commitment is shipping production systems that meet business outcomes, not publishing or chasing the state of the art.
- A role for someone earlier in their engineering career. Real production experience across software engineering, ML or DL, and modern cloud\-native infrastructure is required. Six or more years in the field is the baseline.
Practical details
Based at HHMI Headquarters with a hybrid schedule. Requires a bachelor’s degree or equivalent, plus at least six years of hands\-on software engineering experience, with at least three years building and shipping production machine learning or deep learning systems. Proficiency in Java and Python is required, along with familiarity with agentic AI frameworks such as LangGraph or Strands.
We encourage qualified candidates who are eligible to work in the United States to apply.*Please* *note,* *we* *are not able to* *sponsor a visa for this position* *at this time**.*
\#LI\-EG1
Compensation and Benefits
Our employees are compensated from a total rewards perspective in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, time off, and a range of recognition and wellness programs. Visit our Benefits at HHMI site to learn more.
*Compensation Range*
$146,947\.20 (minimum) \- $183,684\.00 (midpoint) \- $238,789\.20 (maximum)
Pay Type:
Annual
HHMI’s salary structure is developed based on relevant job market data. HHMI considers a candidate's education, previous experiences, knowledge, skills and abilities, as well as internal consistency when making job offers. Typically, a new hire for this position in this location is compensated between the minimum and the midpoint of the salary range.
HHMI is an Equal Opportunity Employer
We use E\-Verify to confirm the identity and employment eligibility of all new hires.
Salary Context
This $146K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Howard Hughes Medical Institute, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $146K to $238K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Howard Hughes Medical Institute AI Hiring
Howard Hughes Medical Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chevy Chase, MD, US. Compensation range: $238K - $238K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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