Interested in this AI/ML Engineer role at Novartis?
Apply Now →Skills & Technologies
About This Role
### Summary
Location: Cambridge or East Hanover
\#LI\-Hybrid 3 days/week in office
Internal Job Title: The Executive Director, Applied AI Lead
Novartis is making a significant long\-term investment in becoming an AI\-first organization, with sponsorship at the highest levels of the company. This is a rare opportunity to shape how generative AI and agentic systems are engineered, deployed and adopted across a global enterprise whose work ultimately supports patients and healthcare systems worldwide. As Executive Director of Applied AI, you will combine technical depth, product thinking and enterprise influence to turn high\-value business challenges into secure, scalable AI products and intelligent workflows. You will lead a small, globally distributed team of applied AI engineers and work directly with senior business and technology leaders to deliver measurable value at scale.
### About the Role
Key Responsibilities
- Define and lead the applied AI deployment strategy, execution roadmap and business integration model across enterprise business units.
- Act as a forward\-deployed AI engineering leader, partnering directly with business teams to translate strategic priorities into deployable products, intelligent workflows and redesigned processes.
- Lead technical solution architecture for complex AI deployments, making hands\-on decisions on application architecture, system integration patterns, model orchestration, APIs, security controls and production infrastructure.
- Lead end\-to\-end implementation, customization, integration and scaling of generative AI, agentic systems and workflow automation solutions in production environments.
- Build, lead and coach a small, globally distributed team of forward deployed AI engineers, establishing engineering excellence through technical review processes, coding standards and career development while actively guiding delivery on critical engagements.
- Establish delivery frameworks and engineering standards that support scalability, reliability, security, maintainability and sustained operational adoption.
- Create reusable AI services, accelerators, evaluation frameworks, blueprints and integration patterns that reduce time to value across business domains.
- Partner with data, infrastructure, information security, architecture, compliance and business teams to align solutions with enterprise priorities and regulated\-environment requirements.
- Track solution performance, adoption and business value through monitoring, feedback loops, user analysis and continuous service optimization.
- Guide modernization of existing systems and processes so they can integrate with agentic AI products, applied AI models and enterprise technology platforms.
- Communicate business impact, technical transformation vision, delivery progress, risks and investment needs to senior executives and key stakeholders.
Essential Requirements
- Bachelor's degree in computer science, engineering, data science or a related field.
- At least 15 years of progressive experience in technology leadership, enterprise AI transformation or complex solution delivery, including leadership of senior technical and cross\-functional teams.
- Deep, hands\-on expertise in applied AI delivery, including taking machine learning, generative AI and agentic solutions from concept through production and enterprise\-scale adoption.
- Strong software and product engineering grounding, with experience integrating AI into complex enterprise platforms, data environments and operational workflows.
- Hands\-on proficiency with modern AI engineering stacks, including Python, cloud platforms (e.g., AWS), LLM orchestration frameworks, vector databases, MLOps/LLMOps and software delivery practices.
- Demonstrated success defining and scaling enterprise\-level AI initiatives, with measurable business value, automation or performance outcomes.
- Experience leading forward deployed, solutions engineering or technical consulting teams, translating ambiguous business problems into production\-grade software systems in enterprise environments.
- Consulting or professional\-services experience partnering with senior technology and business leaders on enterprise transformation, operating models or technology strategy.
- Proven people leadership and organizational development capability, including building high\-performing teams, developing leaders and establishing effective governance.
- Strong executive communication and cross\-functional influence skills, with the ability to align priorities, investment decisions and delivery across a complex matrixed organization.
Desirable Requirements
- Master of Science, engineering postgraduate degree or MBA.
- Experience delivering AI solutions in healthcare, life sciences or another complex regulated environment, or prior zero\-to\-one startup experience.
Benefits \& Rewards
The salary for this position is expected to range between $225,400\.00 \- $418,600\.00 per year.
The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.
Your compensation will include a performance\-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.
US\-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.
To learn more about the culture, rewards and benefits we offer our people click here.
Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people\-and\-culture
Benefits and Rewards: Learn about all the ways we’ll help you thrive personally and professionally.
Read our handbook (PDF 30 MB)
EEO Statement:
The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.
Accessibility \& Reasonable Accommodations
The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e\-mail to \[email protected] or call \+1(877\)395\-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
Division
Corporate
Business Unit
General Management
Location
USA
State
Massachusetts
Site
Cambridge (USA)
Company / Legal Entity
U061 (FCRS \= US061\) Novartis Services, Inc.
Alternative Location 1
East Hanover, New Jersey, USA
Functional Area
Data and Digital
Job Type
Full time
Employment Type
Regular
Shift Work
No
Salary Context
This $225K-$418K range is above the 75th percentile 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 Novartis, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($322K) sits 50% above the category median. Disclosed range: $225K to $418K.
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.
Novartis AI Hiring
Novartis has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $297K - $418K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.