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Location: Hoboken Other locations: Anywhere in Country Salary: Competitive Date: Oct 7, 2026 Job description
Requisition ID: 1750517 Location: Anywhere in Country
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Role Snapshot
Client-facing Life Sciences manufacturing professional who helps design, build, test, and deploy digital solutions for regulated plant operations. The role combines practical knowledge of manufacturing processes with hands-on skills in manufacturing systems, industrial data, integration, analytics, and AI-enabled workflows.
The Opportunity
As a Senior in Supply Chain Manufacturing, you will work with pharmaceutical, biotechnology, and medical-device clients to connect production, quality, laboratory, engineering, and supply chain needs with deployable technology. You will contribute across the delivery lifecycle, from plant and system assessments through requirements, solution design, configuration support, testing, deployment, adoption, and stabilization.
You will work directly with operators, engineers, quality teams, IT and OT specialists, data teams, and technology providers. You will own assigned technical and functional deliverables, help resolve implementation issues, and make clear recommendations to your workstream lead. The role calls for someone who can examine a shop-floor workflow, trace the data and systems behind it, and turn what they learn into a design or testable requirement.
Your key responsibilities
In this role, you will:
- Assess manufacturing and quality workflows, user needs, system interfaces, equipment data, and current-state pain points through site observations, interviews, workshops, and data review.
- Document process flows, user stories, functional requirements, data mappings, interface requirements, controls, and acceptance criteria for digital manufacturing solutions.
- Support the design and implementation of capabilities such as manufacturing execution, electronic batch records and electronic device history records (eDHRs), recipe and routing management, batch and unit genealogy, review by exception, connected-worker workflows, and plant performance reporting.
- Trace information across relevant systems, including MES/MOM, ERP, LIMS, QMS, historians, SCADA, DCS/PLC, and asset-management platforms; identify data gaps, inconsistent definitions, and integration dependencies.
- Develop and review equipment and batch hierarchies, source-to-target mappings, data-quality rules, and contextualized datasets with manufacturing, automation, and data specialists.
- Work with solution architects and engineers on interface specifications, APIs, industrial connectivity, and the movement of data between plant, edge, and enterprise environments.
- Configure or prototype solution components where appropriate to your experience and the engagement, and document design decisions for review by technical and client leads.
- Prepare and execute functional and integration test scripts, record defects, support resolution and retesting, and maintain traceability between requirements and test evidence.
- Support deployment readiness, cutover, user training, hypercare, and handover by preparing clear technical documentation and responding to issues from plant users.
- Partner with client quality and validation teams to prepare or review applicable documentation concerning GxP, data integrity, electronic records, change control, and computer system validation or risk-based assurance.
- Contribute to analytics and AI use cases by assessing source-data quality, defining manufacturing context, checking outputs against operational workflows, and documenting where human review is needed.
- Communicate progress, risks, dependencies, and recommended next steps to client counterparts and engagement leaders; coordinate defined tasks with junior colleagues when needed.
Skills and attributes for success
To thrive in this role, you should be able to work in depth in at least one technical area while collaborating across the broader manufacturing solution. You will bring:
- Manufacturing and quality understanding: Knowledge of how production execution, batch records, equipment, materials, laboratory results, deviations, and quality review connect in a regulated plant.
- Manufacturing technology skills: Hands-on functional, configuration, integration, testing, or support experience with one or more manufacturing, quality, laboratory, automation, or industrial-data solutions.
- Data and integration skills: Ability to identify source systems, interpret batch and equipment data, document mappings and interfaces, investigate data-quality issues, and explain how information moves between OT and enterprise systems.
- Delivery discipline: Ability to produce accurate requirements, design documents, test scripts, defect records, and deployment materials, and to follow issues through resolution.
- Technical communication: Ability to discuss an operational problem with a plant user and document it clearly enough for a developer, data engineer, automation specialist, or tester to act on.
- Regulated-delivery awareness: Understanding of why documentation, traceability, controlled changes, and reliable data matter when a solution supports GxP activities.
- Analytical judgment: Curiosity about AI and advanced analytics, paired with the ability to test whether the underlying data, workflow, and proposed output are fit for use.
To qualify for the role, you must have:
- A bachelor’s degree in engineering, life sciences, manufacturing, information systems, computer science, supply chain, or a related discipline.
- At least 3 years of relevant experience in Life Sciences manufacturing, manufacturing systems, automation, industrial data, systems implementation, technology consulting, or a related field.
- Experience working with manufacturing or quality processes in a pharmaceutical, biotechnology, medical-device, or comparably regulated environment.
- Experience producing technical or functional delivery artifacts, such as requirements, process maps, data mappings, interface specifications, configuration documents, test scripts, or deployment materials.
- Experience collaborating with both operational stakeholders and technical delivery teams to investigate requirements, resolve issues, or implement a solution.
- Ability to travel as needed based on business and role requirements. Travel can range from 80-100%.
Ideally, you’ll also have:
- Experience with MES/MOM, electronic batch records or eDHRs, recipe management, LIMS, QMS, historians, SCADA, DCS/PLC, connected-worker solutions, or industrial data platforms.
- Experience examining manufacturing data from more than one source and relating it to assets, materials, products, orders, recipes, batches, or quality events.
- Familiarity with ISA-88/IEC 61512 batch concepts, ISA-95/IEC 62264 manufacturing information models, or industrial connectivity approaches such as OPC UA and MQTT/Sparkplug.
- Exposure to GxP computer system validation, risk-based software assurance, data-integrity controls, electronic records, or OT cybersecurity considerations relevant to the system being delivered.
- Familiarity with Process Analytical Technology (PAT) and continuous improvement practices, including the use of manufacturing data to monitor process performance, identify variability, and improve quality, throughput, and operational performance.
- Experience supporting analytics or AI use cases in manufacturing or quality, such as process monitoring, predictive maintenance, deviation investigation, or decision support.
- Experience with drug substance, drug product, biologics, sterile manufacturing, medical devices, or technology transfer.
- Relevant platform, cloud, data, automation, Agile, or manufacturing certification.
What we look for
We are looking for a Senior who enjoys getting close to the manufacturing process and the technology that supports it. The strongest candidates ask precise questions, produce dependable technical work, recognize when data or design assumptions do not hold up, and collaborate well with plant, quality, and technology teams to make solutions usable in practice.
What we offer you
At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- The salary range for this job is: + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $116,700 to $194,500 + Bay Area California offices – $121,600 to $202,600 + All other offices locations in the US, including Sacramento – $97,300 to $178,300
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
All in to shape the future with confidence.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
Salary Context
This $116K-$202K 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 EY, 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 $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 ($159K) sits 25% below the category median. Disclosed range: $116K to $202K.
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.
EY AI Hiring
EY has 70 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, AI Safety. Positions span Stamford, CT, Hoboken, NJ, New Brunswick, NJ. Compensation range: $142K - $374K.
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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