Interested in this AI/ML Engineer role at Innercore Health?
Apply Now →Skills & Technologies
About This Role
Description:
ABOUT INNERCORE HEALTH
Innercore Health is an entrepreneurial company that utilizes proven regenerative brain health technology with a mission to improve the health and wellness of millions of people. We are committed to helping people unlock greater resilience, optimize performance, and improve overall well\-being through solutions grounded in science and backed by 14 research studies conducted at the Wake Forest University School of Medicine over the past decade. This purpose\-driven team is looking to make life\-changing impacts every day. This includes an intentional focus on first responders, military service members, athletes, students, and individuals seeking meaningful stress resilience, sleep quality, and performance improvement.
POSITION SUMMARY
The Neurotechnologist (Data \& Analytics Focus) will play a foundational role in building Innercore Health’s data science, neurophysiology analytics, and human performance insights capabilities. This individual will lead the integration and analysis of brain\-based and multi\-modal biometric datasets, helping translate complex physiological signals into actionable insights that drive resilience, recovery, and performance optimization.
This role blends neuroscience, data science, and strategic leadership, contributing to product development, research, and long\-term enterprise data architecture. (Position closes on Tuesday, July 15\.)
Requirements: KEY RESPONSIBILITIES
Neurophysiology \& Brain Data Analytics
- Lead analysis of large\-scale neurophysiological datasets and associated metadata
- Develop analytical frameworks to identify patterns related to:
- Stress response
- Autonomic regulation
- Sleep quality
- Cognitive performance
- Recovery trajectories
- Emotional regulation
- Resilience and allostasis
- Identify biomarkers or physiological signatures tied to performance, readiness, and well\-being
- Collaborate with clinical and scientific advisors to ensure rigor and validity
*Wearables \& Multi\-Modal Biometric Integration*
- Design integration strategies for data from wearable and health platforms, including:
- Oura, WHOOP, Apple Health, Garmin, HRV tools, and sleep trackers
- Build multi\-modal datasets combining:
- Brain activity
- HRV
- Sleep and recovery metrics
- Training load
- Mood and behavioral data
- Environmental/contextual factors
- Develop models linking neurophysiology with functional and biometric outcomes
*University Athletics \& Human Performance Analytics*
- Design data frameworks for collegiate athletic deployments
- Support development of dashboards for athletes, coaches, and performance teams
- Explore applications in:
- Concussion recovery
- Mental resilience
- Overtraining
- Sleep optimization
- Performance readiness
- Establish protocols for longitudinal monitoring and outcome tracking
*Military \& National Security Data Strategy*
- Contribute to future\-state data architecture for military\-scale applications
- Anticipate requirements for:
- Security, scalability, interoperability, and compliance
- Longitudinal monitoring and auditability
- Support frameworks related to:
- Operational readiness
- Cognitive resilience
- Stress exposure and PTSD mitigation
- Sleep disruption
- Human performance optimization
*Data Architecture \& Platform Development*
- Help define enterprise\-wide data architecture strategy
- Guide decisions on:
- Cloud infrastructure
- Secure data storage and HIPAA\-aligned environments
- Data governance and API integrations
- Analytics pipelines and AI/ML environments
- Design scalable systems supporting:
- Multi\-site operations
- Longitudinal data collection
- Research studies and enterprise applications
*AI, Machine Learning \& Predictive Modeling*
- Develop predictive models for:
- Stress recovery
- Sleep optimization
- Burnout risk
- Readiness and performance variability
- Recovery forecasting
- Explore opportunities in:
- Personalized recommendations
- Anomaly detection
- Digital biomarkers
- Adaptive neurophysiology insights
- Support development of AI\-enabled tools for consumer and enterprise use
*Research, Publications \& Scientific Partnerships*
- Support research collaborations and publication\-quality analyses
- Assist with:
- IRB\-aligned data strategies
- Outcomes analysis
- Grant support
- Scientific presentations and conference materials
- Collaborate with academic, clinical, and performance organizations
*Executive \& Strategic Leadership*
- Serve as a strategic partner to executive leadership
- Help shape long\-term data and analytics vision
- Participate in investor and partnership discussions as appropriate
- Contribute to building and leading a future data science organization
REQUIRED QUALIFICATIONS
- Strong experience working with large physiological, biometric, or time\-series datasets
- Experience with wearable device integrations and multi\-modal data systems
- Ability to translate complex technical findings into clear, actionable insights
- Experience designing or contributing to secure, scalable data environments
- Strong collaboration and communication skills across technical and non\-technical teams
COMPETENCIES AND ATTRIBUTES
- Strategic thinking with strong execution capability
- High intellectual curiosity and problem\-solving ability
- Attention to detail and analytical rigor
- Adaptability in a fast\-paced, evolving environment
- Collaborative mindset across scientific, technical, and executive stakeholders
- Mission\-driven with a passion for human performance and brain health
EDUCATION AND EXPERIENCE
- Advanced degree (MS, PhD, MD, or equivalent experience) in a relevant field such as:
- Data Science
- Biomedical Engineering
- Computational Neuroscience
- Bioinformatics
- Applied Statistics
- AI/Machine Learning
- Systems Biology
- Human Performance Analytics
- Background in neuroscience, autonomic physiology, sleep science, or performance analytics preferred
LICENSURE, CERTIFICATIONS
- Not required, but preferred:
- Certifications in data science, machine learning, or cloud platforms
- Experience working within HIPAA or regulated healthcare environments
- Familiarity with SOC 2 and healthcare data security frameworks
PREFERRED TECHNICAL SKILLS
- Python, R, SQL
- Machine learning frameworks (e.g., TensorFlow, PyTorch)
- Time\-series analysis and signal processing
- Cloud data architecture (AWS, Azure, GCP)
- ETL and data engineering pipelines
- Data visualization and dashboard tools
- Experience with healthcare or research data systems
PERSONAL CHARACTERISTICS
- Entrepreneurial and comfortable in a startup environment
- Strategic thinker with long\-term vision
- Curious, mission\-driven, and adaptable
- Strong communicator and cross\-functional collaborator
- Passionate about advancing resilience, recovery, and human performance
POSITION STRUCTURE
Initial engagement may be:
- Fractional
- Advisory
- Project\-based
- Part\-time leadership
- Expected to evolve into a full\-time senior leadership role as the company scales
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 Innercore 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Innercore Health AI Hiring
Innercore Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Winston-Salem, NC, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.