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About This Role
Affiliated Office Address
Baltimore, MD, United States
Requisition ID
121942
Date Created
July 27, 2026
Job Function
Information Technology
Exempt Status
Exempt
Shift Type
Full Time
Schedule
M\-F, 37\.5 hrs wkly
Worksite
02\-MD:Mount Washington Campus
Work Modality
Hybrid: On\-site 60\-89% of hours worked (Ex: 3\-4 out of 5 days/week)
The Johns Hopkins Data Science and AI Institute (DSAI) is a pan\-institutional initiative at Johns Hopkins to advance artificial intelligence and its applications, in part through investments in the software engineering, data science, and machine learning space. DSAI is focused on revolutionizing discovery by advancing artificial intelligence that evolves collaboratively with human intelligence, combining the strengths of each for the betterment of society and the world in which we live. DSAI will bring together the mathematical, computational, and ethical foundations of AI with the domains of Health \& Medicine, Scientific Discovery, Engineered Systems, Security \& Safety, and People, Policy \& Governance.
DSAI seeks multiple *Research Software Engineers* with strong academic and industry background focused on designing and building software for state\-of\-the\-art AI and data science applications across diverse scientific domains. The successful candidates will work at the cutting edge of modern science in collaboration with DSAI affiliated faculty at Johns Hopkins University (JHU) on projects ranging from consulting and short\-term service engagements to large, multiyear AI and data science initiatives and applications. DSAI will address the growing demand for high\-quality professional software engineers within academia who can build dynamic, scalable, open software to facilitate accelerated scientific discovery across disciplines.
The DSAI engineers will be at the forefront of modern data intensive science, where professionally developed software is rapidly becoming a key ingredient for success. The DSAI initiative includes the build\-out of a substantive and professional\-scale software engineering capability.
Specific Duties \& Responsibilities
- Work collaboratively in a team with other RSEs and scientists.
- Participates in ground\-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found in scientific collaborations. The projects may,
- + Require the creation of AI/ML solutions using the latest deep learning libraries trained on state\-of\-the\-art hardware.
+ Involve analysis of massive data sets either in the cloud or on premises.
+ Require creation of novel data science techniques, software pipelines for processing of real\-time high\-frequency data processing workflows and may need the design of complex database models for storing and disseminating scientific data sets.
+ Require deep engagement, possibly leading to co\-authorship on scientific publications, while others may involve a more casual consulting engagement.
+ Require software solutions developed from scratch or refactoring existing solutions to make them conform to industry standards (quality, efficiency, reusability, robustness, portability, documentation, etc.).
+ It is a high\-level goal of DSAI to translate the efforts for individual projects into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.
- Develop software to implement novel scientific research algorithms.
- Create and run data processing workflows utilizing on\-premise or cloud\-based. computing infrastructure.
- Develop data models.
- Co\-author scientific publications describing software and/or other contributions.
- Translate recurring themes from specific projects into frameworks and template patterns. for sustainable scientific infrastructure benefiting future projects.
*Lead and participate in service activities, potentially including*
- Providing guidance to faculty, staff, and students on AI, data science and software engineering.
- Developing and delivering presentations and short courses.
- Attending conferences and workshops.
- Code quality reviews.
- Hiring.
- Other activities as needed.
Special knowledge, skills, and abilities required
- Expert\-level knowledge of Python and/or C\+\+ and willingness to learn other languages as needed.
- Expert\-level knowledge of multiple modern AI/ML, vision, NLP, bioinformatics and/or mathematical or computational libraries.
- Familiarity with software containerization technologies such as Docker and Singularity.
- Familiarity with RESTful web service principles and development.
- Familiarity with SQL and relational database principles and development.
- Fluency in the Linux operating system and related tools.
- Familiarity with modern software engineering best practices, such as Git source control, peer code review, test\-driven development, build automation and continuous integration / continuous delivery.
- Familiarity with cloud development and deployment.
- Demonstrated leadership and self\-direction.
- Willingness to teach others both informally and in short course format.
- Willingness to continually learn new tools and techniques as needed.
- Excellent verbal and written communication.
Minimum Qualifications
- Masters in a quantitative discipline, such as Computer Science, Engineering, Physics or Bioinformatics with strong scientific computing and/or mathematics background.
- Three (3\) year's experience working in software development in large projects and three (3\) year's experience in development and application of
- + AI/ML\- developing, training and applying state of the art models in practical scientific applications aligned with DSAI domains, or
+ Data science \- modeling, transforming, applying ETL pipelines, and similar operations to complex data sets at scale.
- Additional education may substitute for required experience, and additional related experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
Preferred Qualifications
- PhD in a quantitative discipline (highly preferred).
- Five (5\) years’ experience as above in either AI/ML or data science concentration.
- Experience developing, training, fine\-tuning and applying LLMs and/or foundational models.
- Experience deploying AI models onto clinical platforms.
- Experience with large scale scientific simulations or simulations of air/terrestrial/sea vehicles.
- Familiarity with data formats common in scientific domains such as medical imaging, genomic sequences, proteins, chemical structures, geospatial, oceanographic, and heath record data.
- Experience in CUDA GPU programming.
- Experience authoring open\-source Python packages in PyPI.
- Experience in open\-source project governance.
- Experience in open\-source community adoption initiatives.
Classified Title: Scientific Software Engineer
Job Posting Title (Working Title): Research Software Engineer (Data Science and AI Institute)
Role/Level/Range: APPTSTAF/01/ST
Starting Salary Range: Commensurate w/exp.
Employee group: Full Time
Schedule: M\-F, 37\.5 hrs wkly
FLSA Status: Exempt
Location: Hybrid/Mount Washington Campus
Department name: DSAI Institute
Personnel area: Whiting School of Engineering
*Total Rewards*
The referenced base salary range represents the low and high end of Johns Hopkins University’s salary range for this position. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, market conditions, education/training and other qualifications. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits\-worklife/.
*Education and Experience Equivalency*
Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non\-related college course work may be applied towards the total minimum education/experience required for the respective job.
*Applicants Completing Studies*
Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date.
*Background Checks*
The successful candidate(s) for this position will be subject to a pre\-employment background check. Johns Hopkins is committed to hiring individuals with a justice\-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job\-related qualification or function.
*Diversity and Inclusion*
The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion.
*Equal Opportunity Employer*
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
*EEO is the Law*
https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRights6\.12ScreenRdr.pdf
*Accommodation Information*
If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre\-employment process, please contact the Talent Acquisition Office at [email protected]. For TTY users, call via Maryland Relay or dial 711\. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit: https://accessibility.jhu.edu/.
*Vaccine Requirements*
Johns Hopkins University requires all faculty, staff, and students to receive the seasonal flu vaccine. Exceptions to the flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry.
*The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly.*
The pre\-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2\) MMR vaccines; two (2\) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre\-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Johns Hopkins University, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Johns Hopkins University AI Hiring
Johns Hopkins University has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Baltimore, MD, US, Washington, DC, US. Compensation range: $76K - $96K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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