Interested in this AI/ML Engineer role at Bristol Myers Squibb?
Apply Now →Skills & Technologies
About This Role
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.
Position Summary:
As a Senior AI Engineer on the AI Venture Studio team, you will be a hands\-on senior individual contributor who leans into artificial intelligence (AI) to design and build impactful solutions to transform patients’ lives. This role is accountable for the AI\-first engineering of cloud\-native applications, agentic AI products, and the knowledge and context infrastructure that powers them. You will have access to the latest AI\-centric tools and technologies to support activities such as the design of APIs and MCPs, cloud services orchestration, agent runtime deployment, workflow pipeline implementation, and reusable platform pattern development that enables AI Accelerator projects to move fast without giving up reliability, observability, security, or enterprise architectural alignment.
This role lives inside the AI Accelerator delivery model: six fully agile two\-week sprints across a twelve\-week cycle to build, test, validate, and prepare minimum viable products (MVPs) for broader organizational adoption and scaling. AI Accelerator projects focus on the most challenging and highest\-upside pharma\-specific problems across R\&D, Commercialization, Manufacturing, and Enabling Functions where critical context is buried in unstructured knowledge files, multimodal documents and reports, operational records, and scientific evidence packages.
What Matters Most in This Role:
- Ship in cycles by demonstrating engineering progress and lessons learned every two weeks.
- Focus on AI\-first solutioning that prioritizes BMS technology investments (AWS, Claude, LangSmith, etc.) with the best chance of meeting use case and project success metrics.
- Collaborate effectively with other engineers (AI, data, UI/UX, machine learning) and broader agile AI accelerator product teams.
- Demonstrate a curious and inquisitive mindset with broad technical adaptability while staying hands\-on with frontier AI technologies, AI coding agents, and the latest agentic engineering capabilities.
Additional Key Responsibilities:
Cloud\-Native Application and AI Engineering:
- Design, build, and deliver backend services and application components using Python/FastAPI, TypeScript/Node, or similar technologies that integrate LLM APIs, AI agents, retrieval systems, workflow engines, and enterprise systems to create scalable AI\-powered solutions.
- Develop MCP\-accessible services, tools, and skills that enable governed read, write, and search access to structured knowledge assets (e.g., Markdown, YAML), with versioning, auditability, and integration into cloud\-native storage and identity patterns.
- Implement secure application patterns for authentication and authorization, including enterprise SSO, service\-account and machine credential management, secrets management, input/schema validation, and secure service\-to\-service communication.
- Partner with frontend engineers throughout the software delivery lifecycle to define clean API contracts, streaming response patterns, error handling, and service\-level behaviors that enable intuitive AI\-powered user experiences.
Agent Engineering, Orchestration, and Knowledge Systems:
- Build and operate agentic applications using LangGraph, Claude Agent SDK, and related frameworks, including workflow state management, orchestration, tool use, loops, multi\-agent collaboration, and durable execution patterns.
- Develop MCP servers, tools, and skills that expose governed enterprise capabilities to agents through secure, reusable, and observable interfaces.
- Design retrieval, memory, and context architectures using AWS\-native services and data stores, including vector, graph, relational, cache, and object storage patterns that enable grounded and context\-aware AI applications.
- Build evaluation, testing, and observability frameworks that measure agent quality, reliability, latency, cost, and business outcomes while enabling rapid iteration.
- Create reusable platform accelerators, deployment patterns, and golden paths for containerized, serverless, and production AI applications running on AWS.
Platform Engineering, DevOps, and Reliability:
- Build and maintain CI/CD pipelines, infrastructure\-as\-code, automated testing, evaluation frameworks, and release processes for cloud\-native AI applications.
- Measure and improve reliability, quality, latency, cost, and business outcomes through observability, evaluation, and continuous delivery practices.
- Embed security, quality, and reliability controls into delivery pipelines, including automated testing, vulnerability scanning, regression suites, guardrails, and structured\-output validation.
- Create isolated development and execution environments that support safe experimentation, reproducibility, auditability, and governed promotion of code and data assets.
- Develop and maintain reusable platform patterns, tooling, and engineering standards that accelerate the delivery of secure and scalable AI applications.
Technical Leadership, Collaboration, and Delivery:
- Translate business problems into technical architectures and delivery plans that align AI capabilities with measurable product outcomes.
- Drive MVPs toward production readiness by validating technical feasibility, reliability, scalability, security, and measurable business value.
- Rapidly prototype, validate, and iterate on emerging AI capabilities to identify scalable patterns and reduce technical uncertainty before broader adoption.
- Provide technical leadership through architecture reviews, code reviews, design mentorship, engineering standards, documentation, and reusable reference implementations.
- Communicate technical and cloud architecture trade\-offs clearly, balancing speed, cost, reliability, security, compliance, scalability, and long\-term maintainability.
Qualifications \& Experience:
Required Qualifications:
- Bachelor’s or higher degree in Computer Science, Engineering, Science, or a related field.
- 5\+ years of experience designing, building, and scaling software applications, cloud platforms, APIs, or distributed systems with increasing technical responsibility.
- Strong proficiency in Python and FastAPI and/or TypeScript/Node, or comparable backend application frameworks.
- Hands\-on experience building and operating cloud\-native applications on AWS.
- Experience designing and delivering AI\-powered applications that integrate LLMs, retrieval systems, workflows, agents, or other AI capabilities into real\-world user experiences.
- Experience building agentic AI applications using frameworks such as LangGraph, LangChain, PydanticAI, Claude Agent SDK, or similar technologies.
- Experience with containers, CI/CD, GitHub\-based workflows, automated testing, environment configuration, and infrastructure\-as\-code such as Terraform, AWS CDK, or CloudFormation.
- Practical experience integrating enterprise LLM services such as Anthropic (preferred), OpenAI, Gemini, Grok, AWS Bedrock, or similar enterprise\-approved AI services into AI\-powered applications.
- Demonstrated understanding of the architectural patterns, trade\-offs, and engineering practices required to scale AI applications from prototype to production, including reliability, observability, security, performance, and cost management.
- Working knowledge of secure and responsible AI application patterns including authentication, authorization, enterprise SSO, secrets management, auditability, model evaluation, guardrails, and secure application architecture.
- Demonstrated ability to effectively use AI coding agents and AI\-assisted development tools such as Claude Code, Codex, Gemini CLI, Cursor, Windsurf, or GitHub Copilot to accelerate software delivery.
- Strong communication skills and comfort operating within fast\-moving, cross\-functional agile teams where ambiguity, rapid experimentation, and continuous learning are expected.
Desired Qualifications:
- Experience building MCP servers, MCP tools, FastMCP applications, or governed agent tooling ecosystems.
- Experience designing retrieval, memory, context, and knowledge architectures using vector stores, relational databases, graph databases, and enterprise knowledge systems.
- Experience developing multi\-agent systems, workflow orchestration patterns, tool\-calling architectures, durable execution, and long\-running agent workflows.
- Experience with evaluation\-driven development, agent testing, benchmark creation, AI observability, and platforms such as LangSmith, promptfoo, or similar tooling.
- Experience designing semantic layers, natural\-language\-to\-SQL systems, analytics agents, or governed data access patterns.
- Experience building sandboxed execution environments, provenance tracking, version\-controlled data workflows, and audit\-friendly agent architectures.
- Experience deploying, operating, and supporting production AI applications with real\-world users, service\-level objectives, monitoring, incident response, and operational support processes.
- Experience partnering closely with frontend engineers on chat, copilot, citation/provenance, streaming, and human\-in\-the\-loop user experiences.
- Active GitHub contributions, open\-source participation, personal projects, or other demonstrated examples of building with modern AI technologies.
- Experience within life sciences, pharmaceutical, healthcare, scientific, or other regulated industries.
\#AICP
*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:
Cambridge Crossing: $151,280 \- $183,319 Madison \- Giralda \- NJ \- US: $137,530 \- $166,654 Princeton \- NJ \- US: $137,530 \- $166,654 Seattle \- WA: $151,280 \- $183,319
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 adastaffingsupport@bms.com. 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 TAEnablement@bms.com. 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.
R1602673 : Senior AI Engineer
Salary Context
This $137K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $137K to $183K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bristol Myers Squibb AI Hiring
Bristol Myers Squibb has 7 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Princeton, NJ, US, Seattle, WA, US. Compensation range: $106K - $239K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.