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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.
At Medallia, we believe every experience is a memory that can inspire loyalty, trust, and growth. Our platform helps the world's leading brands capture signals across customer and employee journeys and transform them into real\-time action using AI and analytics at enterprise scale. We are building the next generation of AI\-native platform capabilities that power intelligent automation, orchestration, and decision\-making across our products and we need an architect to ensure that evolution happens coherently.
Mission
As the Principal Architect, you will own the end\-to\-end architectural transformation of Medallia from traditional enterprise software into an AI\-native platform company. AI technology and innovation are advancing at a remarkable pace, and we are building AI capabilities across every part of our platform. Your mission is to establish the architectural patterns and practices that ensure this work compounds toward a platform vision rather than fragmented features. Define the Enterprise AI Reference Architecture, draw the boundaries between centralized platform capability and product\-owned innovation, and drive the organizational convergence that makes the platform evolve as one system over the next 3–5 years aligned to emerging standards for AI systems and agent protocols. You are the architect who sees around corners and where the world is going, anticipating what the platform must become when agents, not humans, are the primary actors.
Responsibilities:
Define the Enterprise AI Reference Architecture* Establish standardized agent runtime patterns: orchestration frameworks, agent lifecycle management, and execution environments that product teams build on rather than reinvent.
- Define the memory and context architecture: how customer, session, and domain context is structured, persisted, shared, and scoped across agents and products.
- Set standards for agent communication and interoperability (MCP, A2A, and emerging protocols), eventing, and multi\-agent coordination patterns.
- Design the model abstraction layer: provider\-agnostic interfaces, routing and fallback strategies, and the portability architecture that preserves negotiating leverage and cost flexibility.
- Establish observability and evaluation standards for non\-deterministic systems: tracing, eval harnesses, quality gates, and cost telemetry as first\-class architectural concerns.
Set AI Platform Strategy* Draw and defend the strategic boundary between centralized platform capabilities (runtimes, model gateway, context services, eval infrastructure) and what remains product\-owned , with clear interface contracts between the two.
- Lead build vs. buy decisions across the AI stack and formulate the vendor abstraction strategy that prevents lock\-in at the model, framework, and infrastructure layers.
- Own the platform consolidation roadmap: sequence the convergence of overlapping AI implementations onto shared services without freezing product velocity.
- Maintain the architectural decision record for the AI platform, making trade\-offs explicit, durable, and revisitable as the landscape shifts.
Drive Organizational Convergence* Serve as the primary architectural liaison across Product, Engineering, Data, Security, Infrastructure, Applied AI, and Enterprise Architecture.
- Run the architectural review and exception process for AI initiatives: a lightweight governance mechanism that catches divergence early without becoming a bottleneck.
- Identify and dismantle fragmented AI sprawl, including duplicative agent frameworks, redundant model integrations, and inconsistent context handling through standards, shared services, and influence rather than mandate alone.
- Publish and evangelize reference implementations, golden paths, and architectural patterns that make the standard path the easiest path.
Establish AI Governance \& Operational Standards* Define operational governance for AI systems: prompt and agent lifecycle standards (versioning, review, rollout, rollback), evaluation requirements, and AI incident management.
- Architect the frameworks for model auditability, agent permissioning and identity, and cost governance, making autonomy safe and accountable at enterprise scale.
- Define human oversight boundaries by autonomy class, with deterministic fallback strategies for every autonomous pathway.
- Partner with Security and Compliance to ensure the reference architecture satisfies enterprise, regulated, and government\-cloud requirements by design rather than exception.
Architect for the Agent\-First Future* Continuously pressure\-test the platform with the question: what architecture survives when agents become the primary actors instead of humans?
- Redefine the platform's contracts for that world, with APIs designed for agent consumption, permission models for non\-human identity, state management for long\-running autonomous workflows, and observability that explains agent behavior to humans.
- Anticipate the second\-order shifts: how UX, workflows, and customer interaction models change when customers' agents interact with Medallia's agents, and ensure the architecture is ready before the need is urgent.
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 software engineering experience designing and operating large\-scale distributed systems and platforms, with deep expertise in backend systems, cloud\-native infrastructure, and platform engineering.
- Demonstrated, hands\-on experience building AI/ML infrastructure, agent orchestration systems, or developer platforms in production. You have built these systems, not just evaluated or consumed them.
- Demonstrated experience evolving legacy enterprise architectures toward modern, AI\-centric or autonomous operational models, including the migration strategy and sequencing, not just the target state.
- Demonstrated working fluency with the modern agentic stack: LLM serving and routing, agent frameworks and SDKs, tool\-integration protocols (MCP or comparable), evaluation infrastructure, and context/memory architectures.
- Demonstrated ability to lead complex cross\-functional technical initiatives and to drive adoption of architectural standards through influence, clarity, and credibility rather than authority alone.
- Demonstrated experience authoring Architecture Decision Records (ADRs), reference architectures, and executive narratives for systems impacting engineering teams, with a demonstrated ability to present technical trade\-offs to VP\-level or C\-level stakeholders.
Preferred Qualifications* Deep expertise operationalizing LLMs, multi\-agent frameworks, and autonomous workflow paradigms at enterprise scale (LLMOps/AgentOps).
- Knowledge of AI safety, policy enforcement, and responsible AI operational practices, particularly in compliance\-sensitive or regulated environments.
- Experience with multi\-tenant SaaS platform architecture and the particular challenges of per\-customer configuration, data isolation, and schema variability.
- Track record establishing architecture governance functions (review boards, ADR practices, golden paths) that teams experience as enabling rather than obstructing.
What Success Looks Like* A unified Enterprise AI Reference Architecture exists, is versioned and maintained, and is the default starting point for every new AI initiative, measured by adoption across product and engineering teams in shipped platform projects.
- New AI capabilities ship on shared platform services (runtime, model gateway, context, evals) rather than bespoke stacks; duplicative implementations are measurably consolidated over time.
- Build vs. buy and vendor abstraction decisions are made deliberately and documented ensuring the company can switch model providers or frameworks without re\-architecture.
- AI governance is operational, not aspirational: every production agent has defined permissions, evaluation gates, audit trails, cost accountability, and a deterministic fallback.
- The platform is demonstrably ready for the agent\-first paradigm ahead of demand, with agent\-consumable APIs, non\-human identity, and autonomous workflow support exist before product teams are blocked waiting for them.
Why Join Medallia* Define the foundational AI platform strategy of an industry\-leading enterprise SaaS company at the moment the architecture is being decided.
- Work on deeply technical, high\-impact platform challenges at massive scale with millions of users, global brands, enterprise and regulated environments.
- Influence the future of AI\-native product development across the entire organization; your reference architecture becomes how the company builds.
- Collaborate with exceptional engineers, architects, and product leaders solving complex enterprise problems.
Medallia is committed to equal pay and transparency. The annual base salary range for this position is $229,000 \- $360,000\. This position is bonus eligible. 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 $229K-$360K range is above the 75th percentile 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 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 in Demand for This Role
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 ($294K) sits 63% above the category median. Disclosed range: $229K to $360K.
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.
Medallia AI Hiring
Medallia has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in McLean, VA, US. Compensation range: $260K - $360K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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