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Location: Boston Other locations: Anywhere in Country Salary: Competitive Date: Oct 7, 2026 Job description
Requisition ID: 1750502 Location: Anywhere in Country
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Role Snapshot
Client-facing Life Sciences manufacturing Manager who leads technology-enabled transformation workstreams that improve plant performance, quality, compliance, and supply reliability. The role combines practical manufacturing knowledge with digital, data, AI, and delivery skills to turn operational needs into solutions that work in regulated environments.
The Opportunity
As a Manager in Supply Chain Manufacturing, you will work with pharmaceutical, biotechnology, and medical-device manufacturers to improve how production, quality, laboratory, engineering, and technology teams operate together. You will lead defined workstreams from assessment and requirements through solution design, implementation, testing, adoption, and stabilization.
This is a techno-functional delivery role. You will facilitate decisions with client teams, translate plant-floor needs into actionable requirements, coordinate functional and technical specialists, and take ownership of the quality and progress of your workstream. You will contribute to broader program planning and value realization while working with senior engagement leaders on cross-program decisions and commercial matters.
Your key responsibilities
In this role, you will:
- Lead client-facing workstreams in Life Sciences manufacturing transformation, with clear ownership of plans, deliverables, risks, decisions, and outcomes.
- Assess current-state production and quality processes, user needs, system landscapes, data flows, performance gaps, and site constraints through workshops, data analysis, and plant engagement.
- Translate priorities such as yield, throughput, right-first-time execution, batch-cycle time, deviation reduction, asset reliability, and supply continuity into process designs, requirements, acceptance criteria, and measurable improvements.
- Develop functional designs for relevant capabilities such as batch execution, electronic batch records, recipe management, genealogy, review by exception, connected-worker workflows, maintenance, and performance management.
- Coordinate integration requirements across MES/MOM, ERP, LIMS, QMS, historians, SCADA, DCS/PLC, asset-management systems, and cloud or edge platforms, working with architecture, automation, and cybersecurity specialists.
- Define and review data mappings, asset and batch context, master-data needs, interface specifications, and data-quality controls needed for reporting, analytics, and AI use cases.
- Help deliver analytics and AI use cases, such as process monitoring, predictive maintenance, deviation investigation support, and decision support, by assessing data readiness, workflow fit, evaluation criteria, and human oversight.
- Work with client quality and validation teams to reflect applicable GxP, data-integrity, electronic-record, change-control, and validation or risk-based assurance requirements in workstream deliverables.
- Oversee configuration activities and lead functional testing, defect resolution, deployment readiness, training, cutover support, hypercare, and handover for your workstream.
- Facilitate working sessions across manufacturing, quality, MS&T, engineering, supply chain, IT, OT, data, and technology-vendor teams; escalate decisions and dependencies that affect the broader program.
- Manage workstream scope, schedule, resources, quality, and risks; provide clear status updates and contribute to estimates, proposals, and follow-on opportunity shaping.
- Coach junior team members, review their work products, and capture reusable requirements, design patterns, and delivery lessons.
Skills and attributes for success
You will bring:
- Practical understanding of Life Sciences manufacturing and the connections among production, quality, laboratory operations, engineering, and supply chain.
- The ability to turn operational problems into clear workflows, functional requirements, user stories, data needs, interface requirements, and testable acceptance criteria.
- Working knowledge of manufacturing systems and IT/OT data flows, with enough technical fluency to collaborate effectively with automation engineers, architects, data engineers, developers, and cybersecurity teams.
- Familiarity with batch and equipment data, data contextualization, APIs, historians, and industrial connectivity patterns relevant to the solution being delivered.
- Sound judgment about when analytics or AI is useful, what data and controls it requires, and how people will use its outputs in manufacturing or quality workflows.
- Strong workshop facilitation, structured problem-solving, client communication, team coordination, and delivery-risk management skills.
To qualify for the role, you must have:
- A bachelor’s degree in engineering, life sciences, manufacturing, supply chain, information systems, business, or a related discipline.
- At least 7 years of relevant experience in Life Sciences manufacturing, manufacturing technology, operational excellence, industrial data and AI, technology consulting, or a related field.
- Experience leading a client-facing manufacturing, digital, data, or technology workstream through design and at least one implementation or deployment phase.
- Experience working in a pharmaceutical, biotechnology, medical-device, or comparably regulated manufacturing environment.
- Experience translating manufacturing needs into process, functional, data, integration, or testing deliverables and coordinating business and technical contributors.
- Practical understanding of GxP manufacturing and the documentation, change-control, quality, and data-integrity considerations relevant to digital solutions.
- Ability to travel as needed based on business and role requirements. Travel can range from 80-100%.
Ideally, you’ll also have:
- Experience with drug substance, drug product, biologics, sterile manufacturing, medical devices, or clinical-to-commercial scale-up.
- Delivery experience with MES/MOM, electronic batch records, recipe management, connected-worker tools, industrial analytics, or manufacturing data platforms.
- Familiarity with ISA-88/IEC 61512, ISA-95/IEC 62264, OPC UA/IEC 62541, MQTT/Sparkplug, or unified-namespace approaches as relevant to client systems.
- Awareness of OT cybersecurity practices informed by ISA/IEC 62443 or NIST SP 800-82.
- 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 computer system validation or risk-based computer software assurance where applicable to the regulated product and system.
- Experience with multi-site deployments, plant modernization, technology transfer, or the development of reusable solution assets.
- A relevant program-management, operational-excellence, cloud, data, or manufacturing-technology certification.
What we look for
We are looking for a Manager who is comfortable with plant-floor realities and capable of leading a mixed functional and technical team through implementation. The successful candidate will build trust with client counterparts, produce clear and usable delivery artifacts, recognize quality and technology risks early, and help turn digital and AI investments into sustainable manufacturing improvements.
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 – $154,000 to $256,700 + Bay Area California offices – $160,500 to $267,400 + All other offices locations in the US, including Sacramento – $128,400 to $235,300
- 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 $154K-$267K range is above 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. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $154K to $267K.
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
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
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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