High Performance Coach Jobs
High Performance Coach jobs are open across professional sports, corporate wellness, elite athletics, and military or government performance programs, at every level from entry-level coach to director of performance, with specializations in mental skills coaching, strength and conditioning, and executive performance development. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 2,744+ High Performance Coach jobs
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Position Summary
The Artificial Intelligence (AI) Sr. Data Engineer will design, build, validate, and operationalize AI-enabled solutions that improve infrastructure, technology operations, and enterprise decision-making across hybrid cloud and on-premises environments. The role partners with infrastructure engineering, architecture, operations, cyber/risk, model governance, data science, and product teams to convert business and technology needs into secure, scalable, measurable capabilities.
The ideal candidate combines applied data science, natural language processing, machine learning, automation, model validation, and software engineering experience with the discipline to deliver production-ready solutions in a regulated enterprise environment. This role requires strong technical execution, governance awareness, stakeholder communication, and the ability to move AI/ML capabilities from concept through deployment, monitoring, and continuous improvement.
Key Responsibilities
- Design, develop, test, validate, and deploy AI/ML-enabled capabilities that improve infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
- Apply natural language processing, statistical modeling, supervised learning, unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques to complex enterprise data sets
- Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns across technology, risk, operations, and platform domains
- Support the full model lifecycle, including use case intake, data preparation, model training, model selection, validation readiness, deployment, monitoring, ongoing performance review, and remediation planning
- Provide analytical and technical challenge to AI/ML solutions by assessing model design, assumptions, limitations, performance, controls, explainability, and implementation risks
- Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams to define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
- Develop production-grade code, reusable documentation, model artifacts, validation evidence, test automation, and implementation procedures aligned to enterprise engineering and governance standards
- Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices for AI-enabled infrastructure services
- Communicate technical findings, model outcomes, operational impact, implementation risks, and tradeoffs clearly to engineering teams, senior stakeholders, governance partners, and cross-functional leaders
Required Qualifications
- 15+ years of experience delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions
- 7+ years of hands-on experience applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise business, risk, technology, or operational problems
- Strong Python programming skills and practical experience with data science, machine learning, or NLP libraries such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
- Experience with the end-to-end model lifecycle, including model ideation, data preparation, training, selection, validation, deployment, ongoing monitoring, performance review, and governance documentation
- Experience developing NLP, text analytics, classification, embeddings, recommendation, key driver analysis, network analysis, anomaly detection, or predictive modeling solutions
- Experience creating model documentation, validation evidence, implementation procedures, monitoring plans, governance artifacts, or peer review materials in a large enterprise environment
- Working knowledge of APIs, data pipelines, relational databases, SQL, dashboards, visualization tools, automation frameworks, version control, CI/CD, observability, and production support practices
- Ability to analyze complex structured and unstructured data, identify patterns, convert insights into engineering action, and quantify business or operational impact through metrics and reporting
- Demonstrated experience working in Agile delivery environments using tools such as Jira, Kanban boards, Confluence, and related delivery or documentation platforms
- Excellent written and verbal communication skills, with the ability to explain model behavior, technical findings, operational risks, governance requirements, and implementation tradeoffs to technical and executive audiences
- Highly motivated, self-directed, and comfortable operating across multiple initiatives in a large, matrixed, geographically distributed technology organization
Desired Qualifications:
- BA or BS in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, Artificial Intelligence, Business Analytics, Business Administration, or a related quantitative or technical field; advanced Masters degree preferred
- Experience developing AI/ML solutions for infrastructure operations, capacity forecasting, incident prediction, anomaly detection, root-cause analysis, configuration intelligence, automated remediation, or operational excellence
- Experience with generative AI, large language models, prompt engineering, reusable prompt libraries, AI-assisted workflows, model validation guidance, or GenAI governance practices
- Experience leading or managing data science, NLP, model governance, or AI enablement initiatives across multiple stakeholders or teams
- Experience with enterprise AI infrastructure platforms, model-serving frameworks, GPU or accelerated compute environments, Red Hat OpenShift AI, NVIDIA AI platforms, or comparable AI/ML infrastructure technologies
- Experience integrating AI solutions with enterprise monitoring, observability, workflow orchestration, API, dashboarding, or automation platforms such as Tableau, Streamlit, Shiny, Jupyter, or equivalent tools
- Experience working in regulated environments with model risk management, validation, peer review, data governance, privacy, security, audit, and compliance requirements
- Ability to influence technical direction, establish reusable processes, develop best practices, and communicate effectively with geographically dispersed engineering, operations, architecture, risk, and business partners
Skills:
- Analytical Thinking
- Application Development
- Data Management
- Risk Management
- Solution Design
- Agile Practices
- Architecture
- Collaboration
- Decision Making
- DevOps Practices
- Business Acumen
- Data Quality Management
- Financial Management
- Solution Delivery Process
- Test Engineering
Shift:
1st shift (United States of America)Hours Per Week:
40Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Position Summary
The Artificial Intelligence (AI) Sr. Data Engineer will design, build, validate, and operationalize AI-enabled solutions that improve infrastructure, technology operations, and enterprise decision-making across hybrid cloud and on-premises environments. The role partners with infrastructure engineering, architecture, operations, cyber/risk, model governance, data science, and product teams to convert business and technology needs into secure, scalable, measurable capabilities.
The ideal candidate combines applied data science, natural language processing, machine learning, automation, model validation, and software engineering experience with the discipline to deliver production-ready solutions in a regulated enterprise environment. This role requires strong technical execution, governance awareness, stakeholder communication, and the ability to move AI/ML capabilities from concept through deployment, monitoring, and continuous improvement.
Key Responsibilities
- Design, develop, test, validate, and deploy AI/ML-enabled capabilities that improve infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
- Apply natural language processing, statistical modeling, supervised learning, unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques to complex enterprise data sets
- Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns across technology, risk, operations, and platform domains
- Support the full model lifecycle, including use case intake, data preparation, model training, model selection, validation readiness, deployment, monitoring, ongoing performance review, and remediation planning
- Provide analytical and technical challenge to AI/ML solutions by assessing model design, assumptions, limitations, performance, controls, explainability, and implementation risks
- Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams to define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
- Develop production-grade code, reusable documentation, model artifacts, validation evidence, test automation, and implementation procedures aligned to enterprise engineering and governance standards
- Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices for AI-enabled infrastructure services
- Communicate technical findings, model outcomes, operational impact, implementation risks, and tradeoffs clearly to engineering teams, senior stakeholders, governance partners, and cross-functional leaders
Required Qualifications
- 15+ years of experience delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions
- 7+ years of hands-on experience applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise business, risk, technology, or operational problems
- Strong Python programming skills and practical experience with data science, machine learning, or NLP libraries such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
- Experience with the end-to-end model lifecycle, including model ideation, data preparation, training, selection, validation, deployment, ongoing monitoring, performance review, and governance documentation
- Experience developing NLP, text analytics, classification, embeddings, recommendation, key driver analysis, network analysis, anomaly detection, or predictive modeling solutions
- Experience creating model documentation, validation evidence, implementation procedures, monitoring plans, governance artifacts, or peer review materials in a large enterprise environment
- Working knowledge of APIs, data pipelines, relational databases, SQL, dashboards, visualization tools, automation frameworks, version control, CI/CD, observability, and production support practices
- Ability to analyze complex structured and unstructured data, identify patterns, convert insights into engineering action, and quantify business or operational impact through metrics and reporting
- Demonstrated experience working in Agile delivery environments using tools such as Jira, Kanban boards, Confluence, and related delivery or documentation platforms
- Excellent written and verbal communication skills, with the ability to explain model behavior, technical findings, operational risks, governance requirements, and implementation tradeoffs to technical and executive audiences
- Highly motivated, self-directed, and comfortable operating across multiple initiatives in a large, matrixed, geographically distributed technology organization
Desired Qualifications:
- BA or BS in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, Artificial Intelligence, Business Analytics, Business Administration, or a related quantitative or technical field; advanced Masters degree preferred
- Experience developing AI/ML solutions for infrastructure operations, capacity forecasting, incident prediction, anomaly detection, root-cause analysis, configuration intelligence, automated remediation, or operational excellence
- Experience with generative AI, large language models, prompt engineering, reusable prompt libraries, AI-assisted workflows, model validation guidance, or GenAI governance practices
- Experience leading or managing data science, NLP, model governance, or AI enablement initiatives across multiple stakeholders or teams
- Experience with enterprise AI infrastructure platforms, model-serving frameworks, GPU or accelerated compute environments, Red Hat OpenShift AI, NVIDIA AI platforms, or comparable AI/ML infrastructure technologies
- Experience integrating AI solutions with enterprise monitoring, observability, workflow orchestration, API, dashboarding, or automation platforms such as Tableau, Streamlit, Shiny, Jupyter, or equivalent tools
- Experience working in regulated environments with model risk management, validation, peer review, data governance, privacy, security, audit, and compliance requirements
- Ability to influence technical direction, establish reusable processes, develop best practices, and communicate effectively with geographically dispersed engineering, operations, architecture, risk, and business partners
Skills:
- Analytical Thinking
- Application Development
- Data Management
- Risk Management
- Solution Design
- Agile Practices
- Architecture
- Collaboration
- Decision Making
- DevOps Practices
- Business Acumen
- Data Quality Management
- Financial Management
- Solution Delivery Process
- Test Engineering
Shift:
1st shift (United States of America)Hours Per Week:
40High Performance Coach Jobs by Experience Level
See All 2,744+ High Performance Coach Jobs
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Find High Performance Coach JobsHigh Performance Coach Job Market
Who's Hiring
- Apple132
- Alvarez & Marsal77
- Clients Blackbox71
- NVIDIA52
- Qualcomm33
Top Industries Hiring
- Technology & Software107
- Electronics & Hardware77
- Healthcare & Medical Services38
- Consulting & Professional Services30
- Education25
What Employers Look For
The qualifications that appear most often in high performance coach jobs.
- Bachelor's or master's degree in sports science, kinesiology, psychology, or a related field
- Certification from ICF, NSCA, or an equivalent recognized credentialing body
- Experience designing and delivering performance programs for athletes or high-performing professionals
- Proficiency in performance assessment tools such as force plates, HRV monitors, or psychometric instruments
- Demonstrated ability to track, analyze, and report on individual or team performance metrics
- Strong communication and relationship-building skills for working with coaches, athletes, or executive clients
Tips for Your High Performance Coach Job Search
Tailor your resume to context type
Hiring panels for corporate high performance coach roles weight ROI and behavior-change metrics differently than sports organizations do. Restructure your resume for each context, leading with outcomes that match the employer's performance language, whether that's athletic wins, productivity gains, or injury reduction rates.
Get certified in a recognized framework
Certifications from the International Coach Federation or the National Strength and Conditioning Association signal credibility to hiring managers fast. Many job postings list specific credentials as minimum requirements, so check the listings you're targeting and close any gaps before you apply.
Apply early to roles that fit
Migrate Mate lists high performance coach openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Demonstrate evidence-based methodology in applications
Hiring managers in this field screen for coaches who can articulate the science behind their practice. When writing cover letters or completing application questions, name the specific assessment tools, periodization models, or psychological frameworks you use and tie them to measurable client outcomes.
Prepare a performance case study for interviews
Most high performance coach interviews include a scenario or case-study component. Have one detailed example ready that walks through your diagnostic process, the intervention you designed, how you tracked progress, and the result, so you can adapt it to whatever scenario the panel presents.
Negotiate scope and autonomy, not just pay
High performance coach roles vary widely in how much autonomy you get over program design. During offer conversations, ask specifically about budget authority, athlete or client access, and reporting structure, since those factors shape your ability to deliver results as much as title or compensation does.
High Performance Coach Jobs: Frequently Asked Questions
Which companies are hiring the most high performance coachs?
The companies hiring the most high performance coachs right now include Apple, Alvarez & Marsal, and Clients Blackbox, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of November 2026. Demand is strongest in organizations with structured athlete development pipelines or corporate wellness divisions.
How many high performance coach jobs are remote?
About 62% of high performance coach openings are fully remote or hybrid as of November 2026, with remote availability concentrated in mental skills coaching, executive performance consulting, and program design roles that do not require in-person physical assessment or on-site training delivery.
How do you become a high performance coach?
Start with a degree in sports science, kinesiology, exercise physiology, or applied psychology, then earn a recognized certification such as an ICF credential or NSCA-CSCS. Build hands-on experience through assistant coaching, internships, or applied practice settings, and document client outcomes carefully because evidence of measurable results is what moves your application forward in competitive pools.
How do you get hired as a high performance coach with little experience?
Volunteer or intern with a university athletic department, a community sports club, or a corporate wellness program to build a practice portfolio before applying to paid roles. Employers in this field often weight demonstrated coaching methodology and clear client outcomes over years of paid experience, so documenting your work from unpaid or early-career engagements makes a real difference.
What does the high performance coach interview process look like?
Most processes run through an initial screening call, a panel interview with performance directors or HR, and a practical component where you present a coaching philosophy or walk through a case study. Some organizations add a skills assessment or ask you to design a sample periodization or development plan, so prepare a concrete example that shows your diagnostic and program-design process from start to finish.
Where can I find and apply to high performance coach jobs?
You can find and apply to high performance coach jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your specialization and experience level, then apply directly to each one that fits.
See All 2,744+ High Performance Coach Jobs
Find roles that match your experience and apply in just a few clicks.
Find High Performance Coach Jobs