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About This Role
Omada Health is on a mission to inspire and engage people in lifelong health, one step at a time. The Omada IT department builds and operates resilient, scalable services that empower all of Omada.
Job Overview:
The Staff Technical Program Manager is a senior individual contributor responsible for leading enterprise\-wide AI\-focused business process transformation initiatives that improve operational scalability, efficiency, governance, and cross\-functional execution across Omada.
Reporting to the VP of IT, this role serves as a strategic and operational partner across the organization, working closely with leaders and teams to identify operational challenges, redesign workflows, establish governance frameworks, drive prioritization, and implement scalable operational improvements supported by analytics, automation, and operational best practices.
This role balances strategic program leadership with hands\-on execution, operating as both a consultative partner and delivery lead for complex cross\-functional initiatives. Success in this role requires strong program management discipline, operational thinking, analytical rigor, executive communication skills, and the ability to independently drive large\-scale initiatives in ambiguous and evolving environments.
Program \& Transformation Management:
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- Own and operate Omada's AI Transformation and Business Process Excellence program, including governance models, intake processes, prioritization frameworks, executive reporting, and delivery oversight.
- Lead complex cross\-functional initiatives focused on improving operational scalability, reducing friction, strengthening governance, and optimizing business workflows across the organization.
- Design and manage structured intake and prioritization processes for operational improvement initiatives, including evaluation frameworks based on business impact, complexity, effort, strategic alignment, and operational risk.
- Maintain and govern a portfolio of concurrent transformation initiatives, balancing tactical operational improvements with larger multi\-quarter strategic programs.
- Define and manage program structures including project charters, milestones, dependencies, RAID logs, stakeholder communication plans, executive updates, and success metrics.
- Lead initiatives through the full lifecycle including discovery, current\-state analysis, future\-state design, implementation planning, rollout, adoption, and post\-implementation optimization.
- Establish and maintain operational governance cadences with stakeholders and executive sponsors to ensure alignment, transparency, accountability, and prioritization.
- Drive consistent operational execution standards across transformation initiatives, ensuring projects are well\-scoped, measurable, documented, and aligned with business objectives.
- Develop scalable frameworks, templates, standards, and operating models that improve the maturity and repeatability of process improvement efforts across the organization.
- Provide regular executive\-level reporting on initiative progress, risks, dependencies, operational metrics, and realized business outcomes.
Process Documentation, Redesign \& Operational Excellence:
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- Lead end\-to\-end discovery, documentation, analysis, and redesign of internal business processes across operational, technical, and corporate functions throughout the organization.
- Facilitate workshops and working sessions with stakeholders, SMEs, and operational teams to document current\-state ("as\-is") workflows and design scalable future\-state ("to\-be") operational models.
- Develop and maintain process documentation including BPMN workflows, swimlane diagrams, SOPs, RACI matrices, value stream maps, process narratives, and operational controls documentation.
- Identify operational inefficiencies, process bottlenecks, manual work, handoff failures, duplicate efforts, and governance gaps, recommending pragmatic and scalable solutions.
- Serve as a consultative partner to functional leaders, helping teams improve operational maturity, scalability, accountability, and execution consistency.
- Establish and maintain a governed enterprise process repository with standardized naming conventions, version control, and documentation practices.
- Partner closely with IT, Security, Automation, and Systems teams to translate operational redesign efforts into scalable workflow automation and systems enablement opportunities.
- Support operational readiness and change management activities including communications, stakeholder alignment, documentation, training, rollout coordination, and adoption support.
- Promote a culture of operational excellence and continuous improvement across the organization through repeatable methodologies and collaborative partnership.
- Coach and guide stakeholders on process management best practices, operational governance, and scalable workflow design approaches.
Process Analytics \& Operational Insights:
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- Build and mature Omada's Process Analytics capability, establishing operational KPIs, SLAs, governance metrics, and benefits\-realization frameworks for transformation initiatives.
- Develop operational dashboards and reporting that provide visibility into process performance, cycle times, throughput, automation rates, error trends, operational bottlenecks, and service efficiency metrics.
- Establish measurable pre\-implementation baselines and quantify post\-implementation operational improvements through analytics\-driven reporting.
- Apply data analysis and process mining methodologies to identify inefficiencies, workflow deviations, operational trends, and optimization opportunities.
- Partner with Data, Analytics, and Systems teams to improve operational telemetry, reporting reliability, and visibility into enterprise workflows.
- Use operational data and analytics to support prioritization decisions, helping ensure initiatives are aligned to measurable business impact.
- Present operational insights and executive\-ready reporting to stakeholders and leadership teams in a clear, concise, and actionable manner.
Cross\-Functional Partnership and Services:
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- Partner closely with leaders and teams across the organization to support scalable operational growth, improve execution consistency, and drive operational transformation initiatives.
- Serve as a trusted operational partner to stakeholders, helping translate business priorities and operational pain points into structured transformation initiatives.
- Collaborate with Automation, AI Enablement, Systems, and operational teams to identify opportunities for workflow automation, operational orchestration, and AI\-assisted process optimization.
- Work cross\-functionally to align operational improvements with governance, compliance, security, audit, and systems requirements.
- Facilitate alignment across teams with competing priorities, helping drive consensus and maintain forward progress on complex initiatives.
- Partner with stakeholders to establish operational standards, governance expectations, and scalable execution models across functions.
- Support organizational change initiatives by helping teams adapt to new workflows, systems, governance structures, and operational processes.
- Maintain clear and proactive communication with stakeholders regarding program priorities, initiative progress, operational risks, and transformation outcomes.
What Great Looks Like:
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- Operates as a highly trusted cross\-functional program leader who can independently drive large\-scale operational initiatives from concept through execution.
- Establishes scalable operational frameworks and governance models that improve organizational efficiency, visibility, and execution consistency.
- Successfully balances strategic planning with hands\-on execution, helping teams move from ambiguity to structured operational outcomes.
- Demonstrates strong operational judgment, making pragmatic decisions that balance business priorities, scalability, governance, and delivery speed.
- Builds strong relationships across technical and non\-technical teams throughout the organization, effectively influencing stakeholders without direct authority.
- Improves organizational scalability over time by simplifying workflows, reducing operational friction, and enabling more efficient cross\-functional collaboration.
- Uses analytics and operational insights to drive prioritization, decision\-making, and measurable business outcomes.
- Acts boldly to improve business operations, proactively identifying opportunities to modernize workflows, strengthen governance, and enable automation.
- Delivers reliable outcomes, maintaining high standards of execution, organization, communication, and accountability across initiatives.
- Cultivates trust through transparency, operational discipline, and consistent follow\-through on commitments.
- Supports a culture of continuous improvement by promoting scalable operational practices, strong documentation standards, and repeatable execution frameworks.
- Seeks context before driving change, ensuring operational improvements are grounded in real business needs and organizational priorities.
Success Metrics (First 12 Months):
- Established and operationalized a scalable Business Process Excellence and AI Transformation program with defined governance, intake, prioritization, and executive reporting structures.
- Launched a formalized operational improvement intake and prioritization framework with measurable evaluation criteria and transparent backlog management.
- Built and maintained an enterprise process repository with standardized documentation, governance practices, and version control.
- Delivered multiple high\-impact cross\-functional process transformation initiatives with measurable improvements in operational efficiency, cycle time, scalability, quality, or automation.
- Established operational KPIs, reporting dashboards, and analytics frameworks that provide stakeholders visibility into process performance and transformation outcomes.
- Built strong partnerships across the organization, becoming a trusted leader for enterprise operational improvement initiatives.
- Identified and enabled opportunities for workflow automation, operational orchestration, and AI\-assisted process optimization in partnership with IT and Automation teams.
- Improved operational maturity and execution consistency across teams through scalable governance, process standards, and repeatable operational frameworks.
Candidate Requirements:
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- 10\+ years of experience in technical program management, business process management, operational excellence, consulting, or enterprise transformation roles.
- 5\+ years of experience leading large\-scale cross\-functional programs or operational transformation initiatives within complex organizations.
- Demonstrated experience designing and operating intake, prioritization, governance, or PMO frameworks for enterprise initiatives.
- Strong program and project management expertise, including roadmap planning, dependency management, stakeholder alignment, and executive reporting.
- Deep experience documenting and redesigning business processes using methodologies such as BPMN, swimlane diagrams, SIPOC, value stream mapping, or similar frameworks.
- Strong operational and analytical mindset with experience defining KPIs, SLAs, operational metrics, or benefits\-realization frameworks.
- Experience working with process analytics, process mining, or operational reporting platforms such as Tableau, Celonis, Signavio, Power BI, Looker, or similar technologies.
- Strong consultative and stakeholder management skills, with the ability to influence leaders and drive alignment across teams without direct authority.
- Experience partnering with technical teams on workflow automation, systems implementations, operational tooling, or process orchestration initiatives.
- Strong written and verbal communication skills, including executive presentations, process documentation, operational reporting, and stakeholder communications.
- Ability to independently manage multiple concurrent initiatives in fast\-paced and evolving environments with shifting priorities.
- Experience operating within regulated or compliance\-driven environments such as healthcare, SOC2, HIPAA, or SOX is preferred.
- Familiarity with Lean, Six Sigma, continuous improvement methodologies, or operational excellence frameworks is preferred.
- Familiarity with AI\-enabled operational tooling, intelligent workflow orchestration, or agentic automation concepts is a plus.
- Bachelor's degree in Business, Information Systems, Industrial Engineering, Operations, Computer Science, or related field preferred.
- PMP, PgMP, CBPP, Lean Six Sigma, or equivalent certifications are a plus.
Benefits:
- Competitive salary with generous annual cash bonus
- Equity grants
- Remote first work from home culture
- Flexible Time Off to help you rest, recharge, and connect with loved ones
- Generous parental leave
- Health, dental, and vision insurance (and above market employer contributions)
- 401k retirement savings plan
- Lifestyle Spending Account (LSA)
- Mental Health Support Solutions
- ...and more!
It takes a village to change health care. As we build together toward our mission, we strive to embody the following values in our day\-to\-day work. We hope these hold meaning for you as well as you consider Omada!
- Cultivate Trust. We listen closely and we operate with kindness. We provide respectful and candid feedback to each other.
- Seek Context. We ask to understand and we build connections. We do our research up front to move faster down the road.
- Act Boldly. We innovate daily to solve problems, improve processes, and find new opportunities for our members and customers.
- Deliver Results. We reward impact above output. We set a high bar, we're not afraid to fail, and we take pride in our work.
- Succeed Together. We prioritize Omada's progress above team or individual. We have fun as we get stuff done, and we celebrate together.
- Remember Why We're Here. We push through the challenges of changing health care because we know the destination is worth it.
About Omada Health: Omada Health (Nasdaq: OMDA) is reverse engineering the way healthcare is delivered in America, putting the space between doctor visits–where health is won or lost–at the center of care. Today's healthcare system poorly serves chronic conditions that require ongoing support outside of the exam room, like obesity, diabetes, hypertension, cholesterol, and musculoskeletal conditions. Omada's virtual\-first model combines human\-led care teams, connected devices, and AI\-enabled technology to deliver personalized care at scale, including support for GLP\-1 therapy. Omada has served more than two million members since launch across 2,000\+ employers, health plans, pharmacy benefit managers, and health systems. Learn more at omadahealth.com.
Omada is thrilled to share that we've been certified as a Great Place to Work! Please click here for more information.
We carefully hire the best talent we can find, which means actively seeking diversity of beliefs, backgrounds, education, and ways of thinking. We strive to build an inclusive culture where differences are celebrated and leveraged to inform better design and business decisions. Omada is proud to be an equal opportunity workplace and affirmative action employer. We are committed to equal opportunity regardless of race, color, religion, sex, gender identity, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, domestic partner status, sexual orientation, or any other basis protected by local, state, or federal laws.
Below is a summary of salary ranges for this role in the following geographies:
California, New York State and Washington State Base Compensation Ranges: $161,920 \- $202,400\*, Colorado Base Compensation Ranges: $154,880 \- $193,600\*. Other states may vary.
This role is also eligible for participation in annual cash bonus and equity grants.
- The actual offer, including the compensation package, is determined based on multiple factors, such as the candidate's skills and experience, and other business considerations.
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Salary Context
This $154K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Omada 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $154K to $202K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Omada Health AI Hiring
Omada Health has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist. Positions span Remote, US, San Francisco, CA, US. Compensation range: $178K - $253K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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