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About This Role
Job Overview:
Omada has a dedicated AI Transformation \& Enablement organization focused on embedding AI directly into how business functions operate. Rather than acting as a centralized innovation lab disconnected from day\-to\-day operations, this team deploys senior engineers directly into functions to partner with teams, understand their workflows, and build production AI systems that drive measurable business impact.
Finance is the next major deployment.
As the Sr. Forward Deployed Engineer supporting Finance, you will operate as the embedded AI engineering partner for the Finance organization — attending planning reviews, close cycles, forecasting discussions, operational reviews, and executive preparation processes to deeply understand how Finance operates today and how AI can transform it tomorrow. This is a 01 build with a team behind you, focused on turning ambiguous, high\-value finance problems into production systems used by executives and operators every day. You will own each agent end\-to\-end: from discovery and prototyping, to shipping, monitoring, and iterating.
You will partner closely with the Sr. Director, Strategic Finance, who owns the Finance AI roadmap and business prioritization, while remaining part of Omada's AI Transformation \& Enablement engineering organization, where you will receive technical leadership, architecture guidance, peer collaboration, and engineering support from other Forward Deployed Engineers solving similar challenges across the company.
This role is highly cross\-functional and combines elements of AI engineering, systems integration, workflow automation, product thinking, business partnership, and organizational enablement. You will design, build, deploy, monitor, and continuously improve AI\-powered systems that help Finance teams operate more efficiently, improve accuracy and consistency, accelerate decision\-making, and scale institutional knowledge.
The systems you build will operate within a highly regulated and compliance\-sensitive environment that includes SOX, HIPAA, security, and data governance considerations. As part of the AI Transformation \& Enablement organization, you will help shape the standards, governance patterns, and operational practices that define responsible enterprise AI adoption at Omada.
What You'll Own:
Finance AI Integration \& Knowledge Layer
Design and build the Finance\-specific AI integration layer that connects AI systems to enterprise finance platforms, operational data, and institutional knowledge sources.
This includes:
- Integrating AI systems with platforms such as NetSuite, Adaptive Planning, FloQast, AWS, and internal data warehouses
- Building retrieval and knowledge systems over financial documentation, board materials, KPI definitions, investor communications, and forecasting models
- Developing reusable AI workflow and orchestration patterns for Finance use cases
- Enabling conversational and natural\-language interaction with operational and financial data
- Partnering with central Engineering and AI platform teams to adopt and extend shared AI infrastructure patterns
You will design systems that allow Finance teams to progressively own configuration, evaluation, and operational management of AI workflows over time.
AI\-Powered Financial Quality \& Controls
Build AI\-enabled quality assurance and validation systems that improve confidence, consistency, and operational rigor across Finance workflows.
Examples include:
- Metric reconciliation across reporting materials and presentations
- Narrative and KPI consistency validation
- Financial calculation verification
- Reporting integrity and formatting validation
- AI\-assisted review workflows with human\-in\-the\-loop oversight
- Evaluation and auditability patterns supporting SOX\-aligned processes where applicable
You will help establish evaluation frameworks and review processes that ensure AI systems are reliable, measurable, and operationally trustworthy.
Operational Intelligence \& Automation
Develop AI systems that continuously analyze operational and financial processes to surface insights, anomalies, optimization opportunities, and business risks.
Areas may include:
- Cost and spend monitoring
- Vendor and procurement analysis
- Contract and compliance monitoring
- Pricing and operational trend analysis
- Cloud and AI platform cost optimization
- Automated summarization, alerting, and root\-cause analysis
You will focus on delivering practical AI solutions that improve operational efficiency and support faster, more informed decision\-making.
Market, Earnings \& Strategic Intelligence
Build AI\-powered intelligence capabilities that help Finance leadership monitor external market activity and prepare executive\-level materials more efficiently.
Examples include:
- Monitoring competitor activity, earnings calls, filings, and analyst commentary
- Supporting benchmarking and market intelligence workflows
- Assisting with earnings preparation, executive Q\&A, and board preparation workflows
- Cross\-referencing and validating data across financial narratives, trend reporting, and investor\-facing materials
Financial Workflow \& Narrative Automation
Develop AI\-enabled workflows that accelerate operational execution and reduce manual effort across recurring Finance processes.
Examples may include:
- Monthly and quarterly reporting commentary
- Flash reports and KPI summaries
- Forecast and enrollment analysis
- Revenue and operational model validation
- Executive memo and board material drafting support
You will partner closely with Finance SMEs to ensure workflows remain operationally accurate, transparent, and trusted.
AI Evaluation, Enablement \& Adoption
Enable Finance teams to effectively evaluate, use, and extend AI systems over time.
This includes:
- Building evaluation frameworks, regression testing, and quality scorecards
- Establishing operational review and feedback loops
- Training Finance SMEs on AI evaluation and workflow management
- Promoting transparency around AI capabilities, limitations, and reliability
- Helping Finance teams grow long\-term AI fluency and operational ownership
Success in this role is not just measured by systems you build, but by how effectively the organization adopts and scales them.
Cross\-Functional Partnership \& Operating Model:
This role operates with two closely connected partnership models by design.
From a business perspective, you will be deeply embedded within the Finance organization and aligned to Finance priorities, workflows, operational rhythms, and strategic initiatives. The Sr. Director, Strategic Finance will own roadmap prioritization and business outcomes for Finance AI initiatives.
From a technical perspective, you will remain part of Omada's AI Transformation \& Enablement engineering organization, where you will collaborate with other Forward Deployed Engineers, participate in shared architectural standards, receive technical mentorship and code review, and contribute reusable patterns that scale across other functions.
You will regularly partner across:
- Finance
- Accounting
- IT
- Security \& Compliance
- Data \& Engineering
- Business Systems
- Enterprise AI \& Automation teams
This role requires balancing speed, innovation, governance, operational reliability, and stakeholder alignment in a rapidly evolving AI landscape.
What Makes This Role Different:
- Embedded partnership model: You work directly inside Finance while remaining part of a centralized AI engineering organization
- High ownership environment: You will own initiatives from discovery through deployment and iteration
- Real operational impact: The systems you build will be used daily by Finance operators and leadership teams
- AI\-first transformation work: This is not incremental automation — this role helps redefine how Finance workflows operate
- Strong executive exposure: You will partner closely with senior leadership across Finance, Technology, IT, and Security
- Builder and enabler: Success is measured not only by what you build, but by how effectively the organization adopts and scales it
- Long\-term growth opportunity: This role evolves from hands\-on builder into strategic architect and AI transformation leader over time
Tools \& Technologies:
We do not expect expertise in every tool on day one, but you should be comfortable learning and working across a modern enterprise AI ecosystem.
Examples include:
- AI and orchestration frameworks
- Python services and APIs
- AWS and cloud\-native infrastructure
- Enterprise Finance platforms and operational systems
- Data warehouses and analytics environments
- AI evaluation and observability tooling
- Workflow automation and integration platforms
- Governance, access control, and enterprise AI operational tooling
What Great Looks Like:
- Builds strong trusted partnerships with Finance leaders and subject matter experts
- Delivers production\-ready AI systems that create measurable operational impact
- Balances rapid experimentation with enterprise\-grade reliability and governance
- Creates AI workflows that are understandable, auditable, and operationally sustainable
- Enables Finance teams to independently evaluate and operate AI\-assisted workflows over time
- Operates effectively in ambiguity and turns loosely defined problems into scalable solutions
- Communicates AI capabilities, limitations, and tradeoffs clearly to technical and non\-technical audiences
- Contributes reusable patterns and operational practices that scale AI adoption across the organization
- Demonstrates strong judgment in compliance\-sensitive environments involving financial and operational data
- Embodies Omada values through collaboration, ownership, adaptability, and execution
Candidate Requirements:
AI \& Software Engineering
- 5\+ years of experience building and deploying software, data, automation, or AI\-powered applications
- 2\+ years of recent hands\-on experience building LLM\-based or AI\-enabled systems in production environments
- Strong experience designing AI workflows including retrieval systems, orchestration patterns, tool usage, evaluation frameworks, and multi\-step reasoning systems
- Strong proficiency in Python and experience building production\-quality backend services, APIs, integrations, and automation workflows
- Experience integrating enterprise systems and operational data into AI\-enabled workflows
Enterprise Systems \& Data
- Experience working with enterprise business systems such as ERP, planning, financial, or operational platforms
- Experience designing retrieval or contextual knowledge systems across large document and metric corpora
- Familiarity with structured and unstructured enterprise data environments
- Understanding of operational monitoring, evaluation, and observability concepts for AI systems
Partnership \& Communication
- Excellent communication and stakeholder management skills
- Proven ability to partner directly with non\-technical teams and translate business workflows into scalable technical solutions
- Experience enabling business users through training, documentation, and operational coaching
- Comfortable operating within highly cross\-functional and rapidly evolving environments
Operating Style
- Thrives in ambiguity and 01 environments
- Strong ownership mentality with the ability to independently drive initiatives forward
- Comfortable balancing embedded business partnership with centralized engineering alignment
- Demonstrates strong prioritization and judgment across competing initiatives
Governance, Security \& Compliance
- Awareness of governance, security, and compliance considerations related to enterprise AI adoption
- Familiarity working within regulated or compliance\-sensitive environments involving financial, operational, or healthcare\-related data
- Comfortable incorporating human review, evaluation, and auditability into AI workflows
Nice to Have:
- Experience applying AI to Finance, Accounting, FP\&A, Procurement, Revenue Operations, or operational business workflows
- Experience working with platforms such as NetSuite, Adaptive Planning, FloQast, Workday, or enterprise data warehouse environments
- Experience with AI orchestration frameworks such as LangChain, LangSmith, or similar tooling
- Experience with AWS services, including Bedrock and modern cloud\-native architectures
- Experience extracting structured insights from unstructured enterprise documents using AI techniques
- Prior experience operating as an embedded, Forward Deployed, Solutions, or customer\-facing engineer
- Strong perspective on responsible enterprise AI adoption and organizational AI enablement
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: $142,600 \- $178,300\*, Colorado Base Compensation Ranges: $136,400 \- $170,500\*. 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.
Please click here for more information on our Candidate Privacy Notice.
Salary Context
This $136K-$178K 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. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $136K to $178K.
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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