Interested in this AI/ML Engineer role at Waters?
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About This Role
Overview:
In this role, you'll join the Waters AI Centre of Excellence (CoE) and lead the engineering team responsible for the platform that underpins our AI capabilities.
The team owns platform services, tooling, and operational practices that allow other development teams to build & run their own AI agents and solutions at scale. You'll work closely with architects, security teams, cloud-ops/infrastructure teams, and AI engineers to ensure the platform is secure, reliable, cost-effective, and easy to use. This is a senior technical leadership position. You'll help set engineering standards, guide technical decisions, review critical designs, and support the growth of the engineering team while remaining hands-on where it adds the most value.
We're looking for someone with a strong engineering and platform background who has experience building, delivering, or operating AI solutions in a production environment. While AI experience is important, we recognize that technology is moving quickly and very few people have worked across every area of modern AI platforms. For the right candidate, we're happy to support development in areas where experience may be lighter, particularly as our own AI capabilities continue to grow.
Here at Waters, we look for people who enjoy solving difficult technical problems, work well with others, and focus on delivering outcomes rather than simply delivering technology. Responsibilities:
- Lead the engineering team responsible for the AI platform and its supporting services.
- Own the reliability, security, scalability, and operational health of the platform
- Translate architectural direction into maintainable and supportable engineering solutions
- Implement and operationalize engineering standards through automated testing and quality gates, deployment tooling, and operational readiness practices
- Lead cloud platform, CI/CD, and deployment practices for AI workloads
- Define and maintain monitoring, logging, and observability standards across the platform
- Partner with security, infrastructure, and architecture teams to ensure compliance with enterprise standards
- Balance business priorities, platform improvements, operational stability, and technical debt when planning platform delivery
- Provide mentoring, coaching, and technical leadership to platform engineers
- Improve the developer experience for the AI engineering teams who build agents and AI solutions on the platform; providing reusable services, templates, frameworks, and documentation for them to build on
- Review high-risk technical changes and provide guidance on complex engineering decisions
- Evaluate new technologies and platform capabilities that could improve security, reliability, productivity, or scalability
- Promote knowledge sharing and engineering best practice across the AI CoE
Qualifications:
- 8yrs + experience in software engineering, platform engineering, cloud engineering, machine learning engineering, or a related technical discipline
- Demonstrated experience providing technical leadership for enterprise platforms, distributed systems, or cloud-native solutions
- Experience building, supporting, or operating AI, machine learning, or generative AI solutions in production environments
- Strong understanding of modern software engineering principles, distributed systems, APIs, microservices, event-driven architectures, and cloud-native development
- Experience with at least one major cloud platform (AWS preferred; Azure or GCP experience also welcomed)
- Experience with Infrastructure-as-Code approaches, containerisation, and modern deployment practices
- Experience implementing CI/CD, automation, monitoring, and operational practices for enterprise applications or AI-enabled solutions
- Understanding of AI platform components such as vector databases, retrieval systems, orchestration frameworks, model serving, or related technologies
- Familiarity with agentic AI, AI orchestration frameworks, or emerging AI platform capabilities would be beneficial
- Experience establishing engineering standards, governance practices, and operational processes
- Experience in platform security, access management, risk management, cost and operational governance
- Strong communication and stakeholder management skills, with the ability to work across engineering, architecture, business, and security teams
- Experience mentoring engineers and helping technical teams grow and succeed
Interested, but don't tick every box?
We're looking for experienced engineering leaders who have successfully built, operated, or supported AI solutions in production environments. We don't expect candidates to have worked with every tool, platform, framework, or architectural pattern listed in this description. If you have a strong background in software, cloud, platform, or machine learning engineering and can demonstrate experience delivering AI-enabled solutions, we'd encourage you to apply. We are committed to helping the successful candidate continue to develop their skills as our AI platform, tooling, and operating model mature.
Company Description:
Waters Corporation (NYSE:WAT) is a global leader in life sciences and diagnostics, dedicated to accelerating the benefits of pioneering science through analytical technologies, informatics, and service. With a focus on regulated, high-volume testing environments, our innovative portfolio harnesses deep scientific expertise across chemistry, physics, and biology. We collaborate with customers around the world to advance the release of effective, high-quality medicines, ensure the safety of food and water, and drive better patient outcomes by detecting diseases earlier, managing routine infections, and combating antibiotic resistance. Through a shared culture of relentless innovation, our passionate team of ~16,000 colleagues turn scientific challenges into breakthroughs that improve lives worldwide. Diversity and inclusion are fundamental to our core values at Waters Corporation. It benefits our employees, our products, our customers and our community. Waters complies with all applicable federal, state, and local laws. Qualified applicants are considered without regard to sex, race, color, ancestry, national origin, citizenship status, religion, age, marital status (including civil unions), military service, veteran status, pregnancy (including childbirth and related medical conditions), genetic information, sexual orientation, gender identity, legally recognized disability, domestic violence victim status, or any other characteristic protected by law. Waters is proud to be an equal opportunity workplace and is an affirmative action employer. All hiring decisions are based solely on qualifications, merit, and business needs at the time. Key Words: Platform Engineering; Platform Lead; Platform Architect; Infrastructure As Code; Platform Security; Governance; Technical Leadership; Cloud Ops; LLM; Vector Database; RAG
Salary Context
This $152K-$253K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Waters, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $152K to $253K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Waters AI Hiring
Waters has 3 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in Milford, MA, US. Compensation range: $220K - $302K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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