Interested in this AI/ML Engineer role at Medallia?
Apply Now →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
An exciting opportunity exists within Medallia’s Talent Acquisition Team for a Senior Talent Partner, AI and Tech who is passionate about scaling a global innovator and leader within the AI\-driven experience management industry. 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.
As a Senior Talent Partner, AI and Tech, you will be at the very heart of this mission. You will develop a deep understanding of our business, platform and products and act as a strategic consultant to our senior Engineering and Product leaders. Additionally you will identify, vet, and hire high\-caliber engineering and product management talent across our global footprint. These hires will be critical as we build the next generation of AI\-native platform capabilities to power intelligent automation, orchestration, and decision\-making across our products.
The ideal candidate is a skilled advisor who has had a track record of success owning a portfolio of engineering and product roles requiring AI, Engineering and Data science\-related expertise. If you thrive in a collaborative, smart, and dynamic team that values hiring and candidate experience above all else, we want to connect!
Responsibilities:
- Full\-cycle Pipeline Management: Own and drive the end\-to\-end recruitment process for senior and critical Engineering roles across North America, Latin America, and other regions, as required. There will be a focus on: AI/ML Principal Engineers, Platform Architects, and Product Management roles.
- Project \& Initiative Ownership: Drive and execute priority talent acquisition projects. This will include a range of initiatives including but not limited to: establishing localized employer branding in LATAM hubs and designing standardized technical assessment frameworks.
- Market Intelligence \& Talent Mapping: Conduct proactive research and talent mapping in competitive markets. Provide engineering leaders with real\-time insights on competitor layoffs, talent migration patterns, and salary shifts to guide our approach to hiring in the market.
- Process \& Tech Stack Innovation: Continuously evaluate and optimize Medallia's recruiting processes. Participate in testing and integration of productivity tools, automated sourcing copilots, and AI\-assisted workflows to drive sourcing efficiency.
- Cross\-Border Compensation Strategy: Manage and orchestrate complex offer negotiations in alignment with Medallia’s global policies.
- Data Integrity \& Analytics: Champion data\-driven recruiting by utilizing our ATS (iCIMS) and reporting tools to present pipeline metrics, hiring velocity, and bottleneck analyses directly to leaders at all levels.
Candidates based in the Tysons vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.
Qualifications:
Minimum Qualifications* 7\+ years of tech recruitment experience within the Technology/SaaS industry
- A clear track record of success hiring core and specialized engineering talent (AI, ML, Backend, Distributed Systems, Cloud/Platform, Data, DevOps) and product managers.
- 3\+ years of stakeholder management experience with senior and executive leaders in engineering and/or product.
- Demonstrated experience with iCIMS or other enterprise\-grade ATS, with active exposure to AI\-assisted sourcing platforms or productivity tools.
Preferred Qualifications* AI Co\-Creation / Tool Integration: Experience piloting or configuring advanced AI recruiting workflows (e.g., customized LLM\-driven outreach, automated screening architectures, or advanced database queries).
- Ability to influence, challenge and align upper\-level R\&D management.
- Expertise in LATAM labor laws, tax compliance strategies (local payroll vs. independent contractors), and employee retention trends.
Medallia is committed to equal pay and transparency. The annual base salary range for this position is $145,000 \- $210,000\. 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 ApplicantAccessibility@medallia.com. 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 $145K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($177K) sits 19% below the category median. Disclosed range: $145K to $210K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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: $210K - $210K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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