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About This Role
Department: Technology
Reports to: AVP, Data
Travel: \~10%
Salary Range: $115,200 \- $180,000
Vibrant Emotional Health’s groundbreaking solutions have delivered high quality services and support, when, where and how people need it for over 50 years. Through our state\-of\-the\-art technology\-enabled services, community wellness programs, and advocacy and education work, we are building a society in which emotional wellness can be a reality for everyone.
Position Overview:
The Senior Director, Data Science \& Machine Learning provides strategic and technical leadership for Vibrant's Data Science, Machine Learning, and Artificial Intelligence function. Reporting to the Assistant Vice President, Data, this role is responsible for establishing and leading the organization's data science capabilities in support of the 988 Suicide \& Crisis Lifeline, H2H (Here 2 Help), Community Programs, and enterprise initiatives. The Senior Director oversees the development of data science strategy, applied research, machine learning engineering standards, and AI governance while ensuring analytical solutions are operationally effective, compliant with applicable regulations, and aligned with organizational priorities. This position serves as the senior technical leader for data science and machine learning, providing direction for team development, cross\-functional collaboration, and the responsible implementation of AI\-enabled solutions.
Duties/Responsibilities:
- Define and execute Vibrant's multi\-year data science, machine learning, and AI strategy and roadmap in partnership with Technology leadership.
- Lead and grow the organization's data science and machine learning function, providing mentorship, technical leadership, and career development for team members.
- Translate organizational priorities into a structured applied research and delivery portfolio with measurable outcomes supporting 988 Lifeline, H2H, Community Programs, and enterprise initiatives.
- Advise executive leadership on emerging AI technologies, strategic opportunities, and build\-versus\-buy decisions.
- Represent Vibrant's data science and AI capabilities with executive stakeholders, federal partners, vendors, and external organizations.
- Establish and maintain technical standards for machine learning development, validation methodologies, code quality, documentation, reproducibility, and engineering best practices.
- Coach and mentor data scientists, analysts, and engineers through code reviews, technical guidance, and structured learning opportunities.
- Recruit, onboard, and retain high\-performing data science and AI talent while defining organizational structure and future hiring strategy.
- Foster a culture grounded in scientific rigor, innovation, responsible AI, collaboration, and continuous improvement.
- Lead applied research initiatives that translate analytical findings into actionable recommendations and production\-ready machine learning solutions.
- Design and oversee advanced quantitative research, program evaluation, and statistical modeling supporting organizational and federal reporting requirements.
- Develop and maintain outcome measurement frameworks that evaluate service quality, client outcomes, and operational performance.
- Oversee development, deployment, and monitoring of NLP and machine learning models supporting crisis services, including call summarization, sentiment analysis, quality assurance, risk detection, and routing optimization.
- Partner with engineering teams to ensure machine learning models are deployed within secure, HIPAA\-compliant infrastructure and monitored throughout the model lifecycle.
- Ensure all production AI and machine learning systems meet governance, validation, documentation, audit, and regulatory requirements.
- Serve as the senior technical representative within Data Governance and Responsible AI governance forums, helping establish enterprise AI policies and standards.
- Collaborate with cross\-functional technology, engineering, analytics, governance, and program leaders to ensure machine learning solutions align with operational priorities and clinical appropriateness.
- Support cooperative agreement deliverables, research reporting, and external program evaluation activities.
- Other duties as assigned.
Required Skills/Abilities:
- Executive\-level expertise in statistical modeling, machine learning, natural language processing (NLP), and applied artificial intelligence.
- Deep technical knowledge of end\-to\-end machine learning lifecycle management, including model development, validation, deployment, monitoring, and optimization.
- Demonstrated experience leading and scaling high\-performing data science, machine learning, or AI teams within complex organizations.
- Proven ability to establish technical standards, engineering best practices, and scientific rigor across data science initiatives.
- Strong experience translating applied research into production\-ready machine learning systems that deliver measurable organizational impact.
- Experience designing quantitative research studies, evaluating complex analytical methods, and communicating research findings with appropriate scientific rigor.
- Knowledge of responsible AI frameworks, model governance, model risk management, fairness evaluation, and explainable AI principles.
- Experience working with highly regulated or sensitive data environments, including HIPAA, 42 CFR Part 2, or similar regulatory frameworks.
- Strong ability to partner with executive leadership, engineering, product, analytics, governance, and operational teams to deliver enterprise AI solutions.
- Demonstrated ability to recruit, mentor, develop, and retain technical talent while fostering a collaborative and psychologically safe team culture.
- Excellent written and verbal communication skills with the ability to communicate complex technical concepts to technical and non\-technical audiences.
- Strong strategic thinking, decision\-making, and organizational leadership capabilities.
- Demonstrated commitment to responsible AI, ethical machine learning, equity, transparency, and continuous improvement.
- Experience within healthcare, behavioral health, public health, nonprofit, or other mission\-driven organizations strongly preferred.
Required Qualifications:
- Bachelor's degree in Statistics, Computer Science, Data Science, Epidemiology, Public Health, or a related field required; Master's or Ph.D. strongly preferred.
- 10\+ years of progressive experience in applied data science and/or machine learning engineering.
- Minimum of 5 years of people leadership experience managing data scientists, machine learning engineers, or research teams.
- Demonstrated success building, leading, or significantly scaling a data science, machine learning, or artificial intelligence function.
- Proven experience delivering end\-to\-end production machine learning solutions, including deployment, monitoring, governance, and continuous model improvement.
- Strong technical proficiency with Python and modern machine learning frameworks such as scikit\-learn, PyTorch, TensorFlow, Hugging Face, MLflow, Snowflake, dbt, or comparable technologies.
- Experience working in healthcare, behavioral health, crisis services, public health, or other federally regulated environments strongly preferred.
- Familiarity with HIPAA, 42 CFR Part 2, and governance requirements related to sensitive data.
- Experience with causal inference methodologies, advanced statistical analysis, or program evaluation preferred.
- Experience supporting federal grants, cooperative agreements, or government\-funded programs is highly desirable.
Physical Requirements:
- Must be able to remain in a stationary position 50% of the time.
- Will constantly operate a computer and other standard office equipment.
- May occasionally ascend and descend a ladder to service office equipment or facilities.
- Will frequently communicate over video calls with internal and external stakeholders to provide updates, technical guidance, and project status.
We determine base pay through a comprehensive review of skills, experience, education, certifications, geographic location, and other relevant factors. The range listed reflects the compensation parameters for the role and does not represent the full compensation package. A complete overview of compensation and benefits will be provided by the Talent Acquisition team during the hiring process.
Full time employees will be eligible for excellent comprehensive benefits, including medical, dental, vision, supplemental income insurance, employer paid disability insurance, employer paid life insurance, pre\-tax FSA for medical and dependent care, and 401K available.
Studies have shown that women and people of color are less likely to apply for jobs unless they believe they are able to perform every task in the job description. We are most interested in finding the best candidate for the job, and that candidate may be one who comes from a less traditional background. Vibrant will consider any equivalent combination of knowledge, skills, education and experience to meet minimum qualifications. If you are interested in applying, we encourage you to think broadly about your background and skill set for the role.
Vibrant Emotional Health is an equal opportunity employer. Applicants are considered for positions without regard to veteran status, uniformed service member status, race, creed, color, religion, gender, gender identity, sex, sexual orientation, citizenship status, national origin, marital status, age, physical or mental disability, genetic information, caregiver status or any other category protected by applicable federal, state or local laws.
Please be aware that fictitious job openings, consulting engagements, solicitations, or employment offers may be circulated on the Internet in an attempt to obtain privileged information, or to induce you to pay a fee for services related to recruitment or training. Vibrant does NOT charge any application, processing, or training fee at any stage of the recruitment or hiring process. All genuine job openings will be posted on our careers page and all communications from the Vibrant recruiting team and/or hiring managers will be from an @vibrant.org email address.
Salary Context
This $115K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vibrant Emotional Health, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($147K) sits 31% below the category median. Disclosed range: $115K to $180K.
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.
Vibrant Emotional Health AI Hiring
Vibrant Emotional Health has 2 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $138K - $180K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 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).
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 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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