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Working with Us
Challenging. Meaningful. Life\-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high\-achieving teams. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working\-with\-us .
Summary:
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The Director, Field AI Transformation \& Experience Architecture serves as the strategic design authority for BMS's AI\-enabled field transformation — shaping how capabilities are structured, governed, and experienced across the organization. This role sits within the Field AI Capabilities \& Experience organization and is embedded in the Rewire Field program.
The Director brings cross\-functional fluency, programmatic depth, and field\-grounded perspective to ensure sound architectural decisions — defining how capabilities are built, how accountability flows, and how the field experience is shaped as a result. In addition to this advisory function, the role carries direct business ownership for select AI\-enabled field capabilities within the organization.
Working in close partnership with the Senior Director, Field AI Strategy \& Execution, this role bridges strategic vision and operational execution — translating experience design principles and AI use case priorities into precise business requirements, managing business validation of delivered capabilities, and driving field adoption from pilot through scaled rollout. The Director serves as a credible business partner to IT, product owners, compliance, legal, and all other internal stakeholders involved in capability development and deployment.
Responsibilities:
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### Transformation \& Capability Architecture
- Serve as the team's design authority on AI\-enabled field capability architecture — advising on how field needs are translated into requirements, how accountability is structured across partner organizations, and how the capability lifecycle is governed from ideation through sustained adoption
- Bring enterprise operating intelligence to capability planning — leveraging deep familiarity with enabling systems, data infrastructure, orchestration layers, and compliance frameworks to ensure capabilities are built to survive real\-world execution, not just look sound on paper
- Proactively identify upstream and downstream dependencies in capability planning — anticipating where integration gaps, data readiness issues, and compliance requirements tend to cause late\-stage delivery failures and driving early resolution
### Business Ownership of AI\-Enabled Field Capabilities
- Serve as business owner and delivery lead for select AI\-enabled field capabilities — owning the end\-to\-end business lifecycle from requirements definition through business validation, pilot, and scaled field adoption
- Develop comprehensive, high\-quality business requirements that precisely capture field needs, use cases, workflow integration expectations, and business success criteria — serving as the definitive business specification for the IT organization
- Ensure requirements align with the future\-state field experience vision and AI use case strategy set by the Senior Director — translating strategic intent into actionable business specifications
- Own business acceptance criteria for all AI\-enabled field capabilities — defining what "done" looks like from a business perspective and holding IT delivery to those standards before capabilities reach the field
### Rewire Field Program Architecture
- Advise on the architectural design of the Rewire Field program — shaping how the program is structured, how workstreams are sequenced, how governance operates, and how accountability is distributed across the Field AI Capabilities \& Experience organization and its partners
- Partner with CAPE, IT, and MAICE to establish clear ownership boundaries that prevent scope creep, accountability gaps, and delivery misalignment — ensuring every function understands its role within the program and is held to it
- Serve as the program's architectural conscience — flagging when workstream interdependencies are underestimated, when governance structures create drag rather than clarity, or when the operating model needs to evolve to match new organizational realities
- Enable the Senior Director to operate at strategic altitude by managing cross\-workstream complexity through active cross\-functional engagement and proactive issue resolution — not through process overhead
### Field Experience Architecture
- Own field persona experience mapping — developing, maintaining, and applying detailed end\-to\-end journey maps and experience documentation for AI\-enabled capabilities and field workflows
- Translate experience mapping outputs into actionable business requirements — ensuring journey maps and workflow documentation directly inform IT specifications, rather than existing as standalone artifacts
- Shape how field experience insights flow into program decisions — influencing capability priorities, requirements quality, and change management design through active participation in design reviews, requirement sessions, and adoption planning
Qualifications:
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- Minimum 8 years of experience in pharmaceutical, biotech, or life sciences, with demonstrated depth in transformation program leadership, commercial operations strategy, enterprise program design, or business\-side digital/AI capability development
- Advanced degree (MBA, MS, or equivalent) strongly preferred; Bachelor's degree required
- Demonstrated track record of l eading through influence in a matrixed organization — shaping program design, governance structures, and cross\-functional accountability without relying on positional authority
- Deep working familiarity with enabling data infrastructure, insights orchestration, and compliance frameworks in a pharmaceutical commercial context — gained through hands\-on experience navigating these systems in prior programs
- Proven experience designing and advising on enterprise change management programs for complex, multi\-workstream commercial or digital transformation — with the ability to coach others to execute what they helped design
- Strong structural thinking and systems intuition — naturally identifies how organizational elements connect, where dependencies create fragility, and how program architecture can be resilient without being bureaucratic
- Excellent communication and influence skills — able to translate complex architectural thinking into content that is immediately clear to field\-facing, technical, and senior executive audiences
*If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.*
Compensation Overview:
Princeton \- NJ \- US: $190,680 \- $231,060 \&\#xa;
The starting compensation range(s) for this role are listed above for a full\-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job\-related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.
Eligibility for specific benefits listed on our careers site may vary based on the job and location. For more on benefits, please visit https://careers.bms.com/life\-at\-bms/.
Benefit offerings are subject to the terms and conditions of the applicable plans in effect at the time and may require enrollment. Our benefits include:
- Health Coverage: Medical, pharmacy, dental, and vision care.
- Wellbeing Support: Programs such as BMS Well\-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
- Financial Well\-being and Protection: 401(k) plan, short\- and long\-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
Work\-life benefits include:
Paid Time Off
- US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees)
- Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non\-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays
Based on eligibility\*, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day.
All global employees full and part\-time who are actively employed at and paid directly by BMS at the end of the calendar year are eligible to take advantage of the Global Shutdown.
*\*Eligibility Disclosure:* *T* *he summer hours program is for United States (U.S.) office\-based employees due to the unique nature of their work. Summer hours are generally not available for field sales and manufacturing operations and may also be limited for the capability centers. Employees in remote\-by\-design or lab\-based roles may be eligible for summer hours, depending on the nature of their work, and should discuss eligibility with their manager. Employees covered under a collective bargaining agreement should consult that document to determine if they are eligible. Contractors, leased workers and other service providers are not eligible to participate in the program.*
Uniquely Interesting Work, Life\-changing Careers
With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, promoting global participation in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues.
On\-site Protocol
BMS has an occupancy structure that determines where an employee is required to conduct their work. This structure includes site\-essential, site\-by\-design, field\-based and remote\-by\-design jobs. The occupancy type that you are assigned is determined by the nature and responsibilities of your role:
Site\-essential roles require 100% of shifts onsite at your assigned facility. Site\-by\-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. For these roles, onsite presence is considered an essential job function and is critical to collaboration, innovation, productivity, and a positive Company culture. For field\-based and remote\-by\-design roles the ability to physically travel to visit customers, patients or business partners and to attend meetings on behalf of BMS as directed is an essential job function.
Supporting People with Disabilities
BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to [email protected] . Visit careers.bms.com/ eeo \-accessibility to access our complete Equal Employment Opportunity statement.
Candidate Rights
BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.
If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california\-residents/
Data Protection
We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud\-protection .
Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.
If you believe that the job posting is missing information required by local law or incorrect in any way, please contact BMS at [email protected] . Please provide the Job Title and Requisition number so we can review. Communications related to your application should not be sent to this email and you will not receive a response. Inquiries related to the status of your application should be directed to Chat with Ripley.
R1605356 : Director, Field AI Transformation \& Experience Architecture
Salary Context
This $190K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bristol Myers Squibb, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $190K to $231K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bristol Myers Squibb AI Hiring
Bristol Myers Squibb has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Princeton, NJ, US. Compensation range: $138K - $264K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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