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About This Role
Position Details
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Position Information
Recruitment/Posting Title Lead Software Developer – GPU\-accelerated Free Energy Simulation and Machine Learning Methods
Department Quantitative Biomedicine Inst
Salary Details $93,588 Minimum
Offer Information
The final salary offer may be determined by several factors, including, but not limited to, the candidate’s qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.
Benefits
Rutgers provides a comprehensive benefits package to eligible employees. The specific benefits vary based on the position and may include:
- Medical, prescription drug, and dental coverage
- Paid vacation, holidays, and various leave programs
- Competitive retirement benefits, including defined contribution plans and voluntary tax\-deferred savings options
- Employee and dependent educational benefits (when applicable)
- Life insurance coverage
- Employee discount programs
Posting Summary
The Laboratory for Biomolecular Simulation Research (http://lbsr.rutgers.edu) and Institute for Quantitative Biomedicine (https://iqb.rutgers.edu/) at Rutgers, the State University of New Jersey, is seeking a high\-level software development scientist to create new modern free energy simulation and quantum mechanical/artificial intelligence tools. At Rutgers, the LBSR is directed by Professor Darrin York, and is dedicated to the development and application of innovative biomolecular simulation tools for enzyme design and drug discovery (https://theory.rutgers.edu/). The successful candidate will develop new GPU\-accelerated software for alchemical free energy and new AI\-enhanced quantum mechanical force fields built using integrated semiempirical density\-functional tight\-binding and deep\-learning neural networks. These methods will be integrated into the next\-generation Amber software suite used worldwide. The project is to design and implement new high\-performance software for drug discovery. The successful candidate will work in close contact with Amber developers to implement new methods and optimize code useability and performance that leverages the latest GPU technology. This is thus an outstanding opportunity for an ambitious scientist to be involved as a lead in a project of this scope and impact and is ideally suited for individuals with career aspirations to lead efforts in drug discovery or related new start\-up company efforts.
Position Status Full Time
Posting Number 26FA0635
Posting Open Date 06/26/2026
Posting Close Date 08/31/2026
Qualifications
Minimum Education and Experience
Applicants must hold a PhD in biology (preferred) or chemistry and have postdoctoral experience working with and developing workflows using Amber.
Certifications/Licenses
Required Knowledge, Skills, and Abilities
This is a high\-level software development position that requires proficiency in object\-oriented programming (particularly python and C\+\+, and GPU programming, e.g., cuda) and experience in software engineering and design. In addition, training in the use of molecular simulation methods is requisite, particularly alchemical free energy and/or quantum/machine\-learning methods, as well as high\-performance computing. Deep familiarity with free energy workflows and Amber is essential. Strong written and oral communication skills in English are required, as well as the ability to work independently and prepare scientific manuscripts and/or proposals.
Preferred Qualifications
Equipment Utilized
Software development will involve computer resources at Rutgers and include access to in\-house and national production cyberinfrastructure resources.
Physical Demands and Work Environment
Predominantly operates in an office environment using computer workstation and/or laptop computer, participates in both physical and virtual team meetings, and periodically attends workshops and/or conferences that entail domestic or international travel. Remote work, with regularly scheduled in\-person reporting to the PI, is possible through mutual agreement.
Overview
Applicant is expected to develop software for GPU\-accelerated alchemical free energy and quantum/deep\-learning potential simulations. Duties will involve software development, testing and documentation, applications within drug discovery workflows including performing simulations of relative and absolute ligand\-protein binding, analysis of thermodynamic graphs, maintaining currency in the relevant literature and state\-of\-the\-art technology, and actively engage in a team environment.
Statement
Posting Details
Special Instructions to Applicants
Quick Link to Posting https://jobs.rutgers.edu/postings/277797
Campus Rutgers University\-New Brunswick
Home Location Campus Busch (RU\-New Brunswick)
City Piscataway
State NJ
Location Details
Pre\-employment Screenings
All offers of employment are contingent upon successful completion of all pre\-employment screenings.
Immunization Requirements
Under Policy 100\.3\.1 Immunization Policy for Covered Individuals, if employment will commence during Flu Season, Rutgers University may require certain prospective employees to provide proof that they are vaccinated against Seasonal Influenza for the current Flu Season, unless the University has granted the individual a medical or religious exemption. Additional infection control and safety policies may apply. Prospective employees should speak with their hiring manager to determine which policies apply to the role or position for which they are applying. Failure to provide proof of vaccination for any required vaccines or obtain a medical or religious exemption from the University will result in rescission of a candidate’s offer of employment or disciplinary action up to and including termination.
Equal Employment Opportunity Statement
It is university policy to provide equal employment opportunity to all its employees and applicants for employment regardless of their race, creed, color, national origin, age, ancestry, nationality, marital or domestic partnership or civil union status, sex, pregnancy, gender identity or expression, disability status, liability for military service, protected veteran status, affectional or sexual orientation, atypical cellular or blood trait, genetic information (including the refusal to submit to genetic testing), or any other category protected by law. As an institution, we encourage all qualified applicants to apply. For additional information please see the Non\-Discrimination Statement at the following web address: http://uhr.rutgers.edu/non\-discrimination\-statement
Posting Specific Questions
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Required fields are indicated with an asterisk (\*).
Applicant Documents
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Required Documents
- Resume/CV
- Cover Letter/Letter of Application
Optional Documents
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 Rutgers University, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
Rutgers University AI Hiring
Rutgers University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Piscataway, NJ, US.
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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