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About This Role
Company Description
Press Ganey is the leading experience measurement, data analytics, and insights provider for complex industries—a status we earned over decades of deep partnership with clients to help them understand and meet the needs of their key stakeholders. Our earliest roots are in U.S. healthcare –perhaps the most complex of all industries. Today we serve clients around the globe in every industry to help them improve the Human Experiences at the heart of their business. We serve our clients through an unparalleled offering that combines technology, data, and expertise to enable them to pinpoint and prioritize opportunities, accelerate improvement efforts and build lifetime loyalty among their customers and employees.
Like all great companies, our success is a function of our people and our culture. Our employees have world-class talent, a collaborative work ethic, and a passion for the work that have earned us trusted advisor status among the world’s most recognized brands. As a member of the team, you will help us create value for our clients, you will make us better through your contribution to the work and your voice in the process. Ours is a path of learning and continuous improvement; team efforts chart the course for corporate success.
Our Mission:
We empower organizations to deliver the best experiences. With industry expertise and technology, we turn data into insights that drive innovation and action.
Our Values:
To put Human Experience at the heart of organizations so every person can be seen and understood.
- Energize the customer relationship: Our clients are our partners. We make their goals our own, working side by side to turn challenges into solutions.
- Success starts with me: Personal ownership fuels collective success. We each play our part and empower our teammates to do the same.
- Commit to learning: Every win is a springboard. Every hurdle is a lesson. We use each experience as an opportunity to grow.
- Dare to innovate: We challenge the status quo with creativity and innovation as our true north.
- Better together: We check our egos at the door. We work together, so we win together.
Press Ganey is committed to providing reasonable accommodations to qualified individuals with disabilities or disabled veterans in the hiring process. If you need assistance or an accommodation to apply for a position online or for your interviews or assessments, please contact us at [email protected] Please provide your contact information and the details of your request so we can best assist you.
We are seeking a Staff ML Engineer to lead the design, delivery, and operation of production-grade AI systems. You will build the services and high-throughput pipelines behind our text analytics platform and a growing portfolio of AI products, processing feedback and conversations from every channel our customers use to engage with patients and their customers. The central challenge is sustaining service reliability and measurable AI quality as models, inputs, and volumes change.
As a technical leader, you will guide engineers through design and implementation, contribute directly to the codebase, and lead architectural decisions across teams. Working primarily with Python and cloud-based data and AI platforms, with a growing emphasis on Databricks, you will shape the shared infrastructure supporting both established products and new AI applications.
What You'll Do
- Own technical delivery from prototype through deployment and ongoing production support, partnering with AI Scientists and Product to define requirements, plan implementation, and resolve cross-team dependencies.
- Evaluate proposed AI solutions for production suitability, identify technical risks, and choose architectures that meet quality, reliability, and cost requirements without unnecessary complexity.
- Architect and build high-volume AI services and processing pipelines, including fault tolerance, backpressure, retries, idempotency, and recovery from partial failures.
- Lead the evolution of our Python services and Databricks-based platform for distributed processing, model serving, and integration of traditional ML and LLM-based components.
- Establish production engineering standards for automated testing, CI/CD, model and prompt versioning, load testing, controlled rollouts, and rollback.
- Build evaluation and monitoring capabilities to detect AI quality regressions and track service reliability, throughput, latency, and inference cost.
- Partner with Product and Responsible AI teams to define release criteria and implement requirements for model validation, data privacy, security, and governance.
- Optimize processing and inference workloads, balancing model quality, throughput, latency, capacity, and cost.
- Mentor engineers and lead architecture and code reviews, maintaining consistent standards for software quality and maintainability.
What You'll Bring
- Bachelor's degree in Computer Science, Electrical Engineering, or a related technical discipline, or equivalent practical experience.
- 8+ years of professional software engineering experience, including at least 3 years owning ML or LLM systems in production and their operational support.
- Proven track record of independently leading complex technical initiatives from requirements through production, making architectural decisions and coordinating delivery across stakeholders.
- Advanced proficiency in Python for production services and data processing, with strong SQL skills.
- Experience designing and operating high-throughput distributed systems, with a strong understanding of failure recovery, multi-tenancy, and capacity planning.
- Hands-on experience deploying and operating LLM-based applications, including evaluation, output validation, observability, and cost management.
- Strong production engineering practices across automated testing, CI/CD, monitoring, incident response, and root-cause analysis.
- Demonstrated technical leadership through system design, hands-on implementation, code review, and mentorship.
- Ability to communicate technical decisions and tradeoffs clearly to engineering, research, product, and governance stakeholders.
Preferred Qualifications
- Experience with Databricks or comparable cloud-based data and AI platforms for workflow orchestration, scalable processing, model deployment, and evaluation.
- Experience with NLP, text analytics, or large-scale processing of unstructured data.
- Experience building shared infrastructure for inference, evaluation, and model lifecycle management.
- Familiarity with retrieval-augmented generation, semantic search, and LLM orchestration frameworks.
- Experience with speech-to-text, speaker diarization, or processing conversational audio data.
- Experience deploying and operating cloud-native services on AWS or Azure.
- Experience with healthcare or other regulated environments, including sensitive data handling, auditability, and model governance.
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Press Ganey we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Additional Information for US based jobs:
Press Ganey Associates LLC is an Equal Employment Opportunity/Affirmative Action employer and well committed to a diverse workforce. We do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, and basis of disability or any other federal, state, or local protected class.
Pay Transparency Non-Discrimination Notice – Press Ganey will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.
The expected base salary for this position ranges from $130,000 to $190,000. It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, licensure or certifications obtained. Market and organizational factors are also considered. In addition to base salary and a competitive benefits package, successful candidates are eligible to receive a discretionary bonus or commission tied to achieved results.
All your information will be kept confidential according to EEO guidelines.
Our privacy policy can be found here: https://www.pressganey.com/legal-privacy/
Salary Context
This $130K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Press Ganey, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $130K to $190K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Press Ganey AI Hiring
Press Ganey has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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