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About This Role
Overview:
Medallia is the pioneer and market leader in Experience Management. Our award\-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.
We believe that every experience is a memory that can last a lifetime. Experiences shape the way people feel about a company. And they greatly influence how likely people are to advocate, contribute, and stay. At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees.
We empower exceptional people to create extraordinary experiences together.
Bring your whole self.
The Role and Team
As a Staff TPM (AI) for our R\&D PMO, you will lead the execution of our most critical Product development initiatives with AI embedded throughout the lifecycle. We lead both customer\-facing GenAI product and platform programs and internal AI\-enabled engineering transformation initiatives that improve how we build, operate, and deliver value. The Medallia R\&D PMO team maintains a strong and strategic partnership with Product and Engineering, and together we are building solutions that are moving the Experience industry forward, delivering solutions that our customers love.
This is first and foremost a Technical Program Management (TPM) role. The ideal candidate brings strong TPM discipline, excellent cross\-functional leadership, and AI fluency to work with business leaders on initiatives that automate workflows, improve decision\-making, and increase productivity through AI and workflow automation capabilities.
Responsibilities:
- Lead multiple concurrent strategic initiatives and programs varying in size, scope, complexity and ambiguity, with cross\-functional and cross\-organizational dependencies.
- Collaborate with multiple Product and Engineering teams across platform and applications
- Take responsibility for the success and health of the Strategic Initiatives under your care and act as a change agent to drive operational efficiencies into process, people and tooling.
- Proactively manage technical challenges \& risks in all assigned areas; work with teams and management to mitigate these risks before they become issues.
- Be sought after by leadership for “the real story” and craft communications that can be consumed at different levels of the organization.
- Propose solutions and consult on system changes. Influence long lasting architecture along with documentation.
- Ability to represent R\&D by answering both technical and business\-related questions on behalf of the team.
- Continually pursue delivery excellence by improving predictability, efficiency and throughput.
- Understand hybrid\-cloud architecture, infrastructure, systems development, and cloud computing.
- Amplify your impact by educating and mentoring the TPM, Operations, and Engineering communities on program management best practices.
- Identify opportunities to improve execution of the organization as a whole, to increase Operations and Engineering productivity through tools and process improvements.
- Understand the business metrics and operational controls required for production GenAI capabilities, and coordinate across teams to ensure telemetry for usage \& monitoring, and fallback and rollback mechanisms
Candidates based in the Tysons vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.
Qualifications:
Minimum Qualifications* 10\+ years of leadership experience in Product / Engineering / PMO
- 5\+ years of demonstrated experience working closely with C\-level executives
- 3\+ years of experience in Cloud / SaaS
- Demonstrated success leading and managing large\-scale initiatives across organizations in a matrixed, globally\-diverse workforce
- Demonstrated experience in delivering AI/ML or GenAI products or features from use\-case definition thru production launch and post\-launch iterations
- Experience with AI model lifecycle
- Exposure to responsible AI governance, and partnership with Security and Legal
- Hands\-on experience with GenAI development and productivity tools such as Claude, Cursor, etc. is expected.
- Hands\-on experience applying SDLC, Project Management, and Product Life\-Cycle methodologies and configuring project management tooling (e.g. Jira and Confluence) to track roadmap progress, risk and deliverables.
- Business Value \& Metrics: Experience defining Key Performance Indicators, calculating ROI, and mapping technical program outcomes directly to business revenue, cost savings, or operational efficiency metrics.
- Strategic \& Tactical Execution: Track record of owning a technical program lifecycle from initial strategy and roadmap definition through to day\-to\-day execution, tracking, and delivery.
- Systems \& Process Architecture: Experience managing multi\-million dollar or multi\-team programs where you successfully integrated technical architecture, operational workflows, and business processes across distinct business units.
- Executive Communication: Experience presenting program status, technical risks, and strategic trade\-offs to VP and C\-level executives, and translating complex technical concepts for non\-technical leadership and stakeholders.
Preferred Qualifications* 8\+ years of people management skills
- PMP, CSM, CPO, PgMP, IAPM, CISSP, ITIL, Six Sigma certifications
- Experience with hybrid cloud solutions
- Acquisition Integration experience
- Adept at cultural change management
- Curious, adaptable, and action\-oriented in applying AI to real business problems and measurable outcomes.
- Ability to thrive and succeed in a dynamic environment not slowed down by ambiguity or competing priorities
- Able to effectively modulate the presentation of information based on your audience: everyone from a C\-level executive to an Individual Contributor
Medallia is committed to equal pay and transparency. The annual base salary range for this position is $139,000 \- $222,000\. Please note that the salary range information provided is a general guideline and combines all of the distinct labor markets within the US. It is uncommon for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on a variety of factors. Medallia considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s work experience, candidate’s work location, education/training, key skills, internal peer equity, external market data, as well as, market and business considerations when making compensation decisions.
Medallia also offers competitive health and wellness benefits, including but not limited to medical, dental, vision, 401(k), short\-term and long\-term disability, life and AD\&D insurance, statutory leaves, paid parental leave, and paid holidays. Benefits and eligibility may vary by location and role.
At Medallia, we celebrate diversity and recognize the value it brings to our customers and employees. Medallia is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age (40 and over), disability, genetic information, veteran status or military service, or any other status protected by state or local law. Individuals with a disability who need an accommodation to apply please contact us at [email protected]. For information regarding how Medallia collects and uses personal information, please review our Privacy Policies. Applications will be accepted for 30 days from the date this role was posted or until the role has been filled.
Salary Context
This $139K-$222K range is above 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 Medallia, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $139K to $222K.
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.
Medallia AI Hiring
Medallia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $222K - $222K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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