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Posting Details
Position Information Job Title Artificial Intelligence (AI) Research Scientist On-Call
Posting Number S1723P
Pay Range Compensation for this position is expected to be $65.00 per hour, in alignment with what California Baptist University (CBU) reasonably anticipates paying for this role. The final rate may vary depending on the candidate’s qualifications, experience, and prevailing market conditions. In addition to competitive wages, CBU provides a comprehensive and generous benefits package to eligible employees.
Position Summary Information Summary Reporting to the DINA Lead, the Artificial Intelligence (AI) Research Scientist On-Call position will play a key role in the development and advancement of an AI-driven Decision Support System (DSS). This position is responsible for designing, developing, and integrating advanced AI technologies, including real-time data processing, retrieval-augmented generation (RAG), Speech-to-Text (STT), and Text-to-Speech (TTS) models and APIs to enhance decision-making capabilities across diverse industries. The AI Research Scientist will collaborate with a multidisciplinary team of Ph.D.-level AI experts specializing in multi-cloud architectures (Azure, AWS, and Google Cloud Platform), natural language processing (NLP) pipelines, scalable knowledge graphs, and AI-driven agent frameworks. The role requires conducting applied and translational research, developing innovative AI solutions, publishing and disseminating findings where appropriate, and contributing to the deployment of scalable, production-ready systems. In addition to technical contributions, the AI Research Scientist will provide mentorship and guidance to student team members, supporting their professional development, research engagement, and technical skill development while fostering a collaborative learning environment. ON-CALLPOSITIONDEFINITION: An On-Call position at California Baptist University (CBU) is a temporary, part-time assignment established to provide staffing support based on departmental and organizational needs. Employees in on-call positions are generally not expected to work more than twenty-nine (29) hours per week on a continuous basis. Work schedules are determined by operational requirements and may vary from week to week and month to month. The duration of employment is based on business needs and may be modified, extended, or concluded at the discretion of the University. Employees will be notified of anticipated changes to their work schedule or assignment duration as business needs evolve.
Essential Duties and Responsibilities include the following. Other duties may be assigned.
- Research, develop, validate, and deploy AI models, algorithms, and data pipelines for processing large-scale structured and unstructured datasets, leveraging natural language processing (NLP), knowledge graphs, and semantic search technologies.
- Design, implement, and optimize secure, scalable, and high-performance AI solutions across multi-cloud environments, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).
- Enhance and maintain retrieval-augmented generation (RAG) frameworks to improve knowledge retrieval, contextual understanding, and recommendation accuracy.
- Develop and optimize integrations with Neo4j, vector databases, and related technologies to support dynamic knowledge graph applications and advanced data relationships.
- Lead the research, development, fine-tuning, evaluation, and benchmarking of large language models (LLMs) to support automated decision-making and intelligent recommendations.
- Design, build, and deploy AI-powered agents capable of interpreting complex queries, executing automated workflows, and delivering context-aware insights and recommendations.
- Develop, integrate, and maintain Speech-to-Text (STT) and Text-to-Speech (TTS) models, services, and APIs to enable advanced voice-enabled AI interactions.
- Design and implement secure AI workflows, data governance practices, and privacy controls to support the handling of sensitive and enterprise-level data.
- Collaborate with software engineers, data scientists, and cross-functional stakeholders to improve automated document ingestion, classification, information extraction, and data management processes.
- Conduct applied research, evaluate emerging AI technologies, author technical reports and scholarly publications as appropriate, and identify opportunities for innovation and continuous improvement.
- Mentor student researchers, graduate assistants, and junior team members by providing technical guidance, research mentoring, and professional development support.
Supervisory Responsibilities This position does not have direct supervisory responsibility but may provide mentorship, technical leadership, and project guidance to student researchers, graduate assistants, and other project team members.
Other Knowledge Skills and Abilities To perform this position successfully, an individual must be able to effectively carry out all essential duties. The following requirements outline the knowledge, skills, and abilities needed for the role:
- Demonstrated commitment to living out and upholding the University’s Christ‑centered mission and values.
- Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, Computer Engineering, Electrical Engineering, Natural Language Processing (NLP), or a closely related field.
- Minimum of five (5) years of experience in AI/ML research, development, and deployment, with a strong emphasis on enterprise-scale and multi-cloud AI solutions.
- Advanced proficiency in Python, PyTorch, TensorFlow, LangChain, DSPy, or equivalent AI development frameworks.
- Expertise in natural language processing (NLP), retrieval-augmented generation (RAG), semantic search, knowledge graphs, and graph-based reasoning systems.
- Extensive experience with Neo4j, vector databases, vector search technologies, and scalable knowledge management architectures.
- Proven track record of designing, developing, and deploying AI-powered agents, autonomous systems, and workflow automation solutions.
- Deep expertise in Speech-to-Text (STT) and Text-to-Speech (TTS) technologies, including model development, fine-tuning, optimization, and API integration.
- Strong understanding of federated AI architectures, data governance, cybersecurity principles, privacy regulations, and compliance requirements for AI systems.
- Experience designing and implementing AI solutions across major cloud platforms, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).
- Experience with model evaluation, AI safety, explainability, and responsible AI principles.
- Demonstrated ability to collaborate effectively within interdisciplinary research and development teams, while providing mentorship and technical guidance to student researchers and junior team members.
- Strong analytical, problem-solving, communication, and project management skills, with the ability to manage multiple priorities in a dynamic research environment.
PHYSICALDEMANDS: The physical demands described herein are representative of those required for an employee to successfully perform the essential functions of this position. The employee is regularly required to sit, stand, walk, and use hands and fingers to operate a computer, keyboard, and other standard office equipment. The position requires sufficient visual acuity to read printed and electronic materials, analyze data, and perform computer-based work. The employee must also possess the ability to communicate effectively in person, virtually, and by telephone. Occasional lifting, carrying, and moving of materials weighing up to fifteen (15) pounds may be required. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions of the position.
WORKENVIRONMENT: The work environment characteristics described herein are representative of those encountered while performing the essential functions of this position. Work is primarily performed in a professional office or research setting with the use of computer and information technology systems. The noise level is generally moderate and consistent with a typical office environment. The position involves minimal exposure to adverse environmental conditions or health and safety hazards. The employee may be required to participate in virtual meetings, collaborative research activities, and occasional on-campus events or training sessions as needed to support departmental and organizational objectives.
Education and/or Experience EDUCATION and/or EXPERIENCE: A Ph.D. from an accredited institution in Artificial Intelligence, Machine Learning, Computer Science, Computer Engineering, Electrical Engineering, Natural Language Processing (NLP), Data Science, or a closely related field is required. Candidates should possess five (5) to ten (10) years of progressively responsible experience in AI/ML research, development, and deployment, or an equivalent combination of education, research, and professional experience that demonstrates the knowledge, skills, and abilities necessary to perform the essential functions of the position. Experience leading or contributing to interdisciplinary research and development initiatives involving advanced AI technologies is highly desirable. PREFERREDEDUCATION and/or EXPERIENCE: Candidates with experience designing, developing, and deploying enterprise-scale AI applications and intelligent decision support systems are strongly preferred. The ideal candidate will possess a robust research background demonstrated through peer-reviewed publications, conference presentations, patents, or other scholarly contributions in artificial intelligence, machine learning, natural language processing (NLP), knowledge graphs, or AI-driven decision support systems. Experience with transformer-based language models and modern AI frameworks, such as BERT, GPT, STORM, DSPy, or similar architectures, is highly desirable. Experience with Claude, Gemini, Llama, Mistral, and related open-source or commercial foundation model ecosystems is highly desirable. Preference will also be given to candidates with hands-on experience in retrieval-augmented generation (RAG), agentic AI systems, vector databases, large language model (LLM) fine-tuning, MLOps practices, model monitoring, and the deployment of AI solutions in production environments. Experience securing external research funding, contributing to grant proposals, or participating in sponsored research programs is preferred.
Posting Detail Information Open Date
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Open Until Filled
Special Instructions to Applicants
Nondiscrimination Statement State and Federal law permit California Baptist University to discriminate on the basis of religion in order to fulfill its purpose. The University does not discriminate contrary to either State or Federal law.
Applicant Documents
Required Documents
- Resume
- Cover Letter
Optional Documents
- Letter of Reference 1
- Letter of Reference 2
- Other Document
- Other Document 2
- Christian Experience Essay
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 9,653 AI roles we're tracking, Research Scientist positions make up 2% of the market. At California Baptist University, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 708 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
California Baptist University AI Hiring
California Baptist University has 1 open AI role right now. They're hiring across Research Scientist. Based in Riverside, CA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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