Interested in this AI/ML Engineer role at Insmed Incorporated?
Apply Now →About This Role
At Insmed, every moment and every patient counts — and so does every person who joins in. As a global biopharmaceutical company dedicated to transforming the lives of patients with serious and rare diseases, you’ll be part of a community that prioritizes the human experience, celebrates curiosity, and values every person’s contributions to meaningful progress. That commitment has earned us recognition as *Science* magazine’s No. 1 Top Employer for five consecutive years, certification as a Great Place to Work® in the U.S., and a place on *The Sunday Times* Best Places to Work list in the UK.
For patients, for each other, and for the future of science, we’re in. Are you?
About the Role:
As AI rapidly transforms the life sciences landscape — from drug discovery and clinical development to commercial operations and patient engagement — we are seeking an Executive Director, AI Transformation \& Strategy to ensure that AI is not an afterthought, but a strategic starting point for how the organization operates and competes.
This role sits at the intersection of business strategy, emerging technology, and operational transformation, partnering with leaders to identify high\-impact transformational AI opportunities and through the co\-creation process with the business, translates them into value generating solutions that benefit Therapeutic Area and functionals teams.
This is not an IT or engineering role — it is a senior business leadership position for someone who understands the life sciences value chain and can translate AI's potential into competitive advantage, operational transformation, and measurable business outcomes.
What You'll Do:
In this role, you will develop and steward the organization's enterprise AI transformation strategy beyond incremental change to reimagine the ways in which we work, ensuring alignment with corporate goals, patient\-centric mission, and competitive positioning in the life sciences sector
You will also have responsibility for:
AI Strategy \& Vision
- Partner with business leaders to identify, prioritize, and activate high\-value AI use cases, translating business problems into well\-scoped opportunities for the teams to execute
- Act as a bridge between non\-technical business stakeholders and technical teams (data science, IT, digital), ensuring AI solutions will impact both operations and strategy, allowing us to increase the speed and quality of program progression to help get medicines to patients sooner
- Translate AI capabilities into business value narratives that resonate with non\-technical stakeholders across functions
- Together with the CIO, and VP of IT Clinical, Commercial \& Emerging Technology serve as a thought partner to the Executive Committee on AI adoption, fluency, and organizational readiness
Cross Functional Enablement and Change Management
- Partner with Therapeutic area, Program and Functional leaders to embed AI into operating models, workflows, and decision\-making processes
- Drive a mindset shift across the organization — moving AI from a peripheral tool to a core strategic capability
- With IT and Organizational Transformation, champion AI literacy and upskilling programs for business leaders and employees, including certification pathways and learning modules
- Articulate the need for change through facilitating working sessions, leadership summits, and forums to accelerate AI adoption and share real\-world use cases
Governance and Responsible AI
- As part of the AI Governance Committee, collaborate with IT, Legal, Compliance, and Regulatory Affairs to ensure responsible, ethical, and compliant AI deployment across the enterprise
- Collaborate and align with the Cybersecurity team to develop robust data protection protocols and policies for secure data handling practices for all AI systems processing sensitive information
- Establish governance frameworks for AI model oversight, bias mitigation, and transparency
- Ensure alignment with FDA, EMA, and other relevant regulatory guidance on AI/ML in life sciences
Stakeholder Engagement and External Positioning
- With CPSO, CIO and VP of IT Clinical, Commercial \& Emerging Technology, represent the organization at industry forums, conferences, and consortia focused on AI in biopharma
- Stay current on the evolving AI landscape — including generative AI, agentic AI, and AI\-powered drug development — and translate trends into strategic recommendations
Performance and Impact Measurement
- Own the AI business case process — working with Finance and business owners to develop pilots and experiments, define success metrics at different stages, track ROI, and communicate impact to senior stakeholders.
- Maintain a portfolio view of AI investments across the organization, ensuring resources are allocated to the highest\-priority initiatives and that efforts are not duplicated.
- Define and track KPIs and ROI metrics for AI initiatives across business functions
- Report progress to executive leadership and the Board on AI strategy execution and business impact, knowing that more projects does not equal more value and the assessment of in\-house vs third\-party, and first\-generation vs next\-generation are critical to the value proposition
- Continuously assess organizational AI maturity and benchmark against industry peers
Who You Are:
You hold a bachelor’s or master’s degree with at least 15 years of progressive business leadership experience in life sciences (Pharmaceuticals, Biotech, Medtech, or CRO/CDMO), ideally with deep knowledge of at least two major functional areas (e.g. R\&D, Commercial, Regulatory, Operations, etc).
In addition, you will have:
- A track record of leading complex, cross\-functional strategic initiatives and several of which are AI related, from concept through execution — you know how to get things done in matrixed organizations.
- Demonstrated ability to evaluate emerging technologies through a business lens — you don't need to write code, but you know the right questions to ask, can spot hype from substance, and can hold vendors and internal teams accountable for outcomes.
- Exceptional communication and influence skills; you are as comfortable presenting to the Executive Committee as you are facilitating a working session with frontline managers.
- Experience with data\-driven decision\-making and comfort with concepts like AI/ML, automation, and predictive analytics at a strategic level.
- Familiarity with relevant regulatory frameworks affecting AI use in life sciences (e.g., FDA guidance on AI/ML in drug development or medical devices) is strongly preferred.
Travel Requirements
This role requires domestic and international travel of approximately 20% based on business needs.
Where You'll Work
This is a hybrid role based out of our Bridgewater, NJ office. You’ll work remotely most of the time, with in\-person collaboration when it matters most.
\#LI\-SK1
\#LI\-SK \- Hybrid
Pay Range:
$255,000\.00\-347,500\.00 Annual
Life at Insmed
At Insmed, you’ll find a culture as human as our mission—intentionally designed for the people behind it. You deserve a workplace that reflects the same care you bring to your work each day, with support for how you work, how you grow, and how you show up for patients, your team, and yourself.
Highlights of our U.S. offerings include:
- Comprehensive medical, dental, and vision coverage and mental health support, annual wellbeing reimbursement, and access to our Employee Assistance Program (EAP)
- Generous paid time off policies, fertility and family\-forming benefits, caregiver support, and flexible work schedules with purposeful in\-person collaboration
- 401(k) plan with a competitive company match, annual equity awards, and participation in our Employee Stock Purchase Plan (ESPP), and company\-paid life and disability insurance
- Company Learning Institute providing access to LinkedIn Learning, skill building workshops, leadership programs, mentorship connections, and networking opportunities
- Employee resource groups, service and recognition programs, and meaningful opportunities to connect, volunteer, and give back
Eligibility for specific programs may vary and is subject to the terms and conditions of each plan.
*Current Insmed Employees: Please apply via the Jobs Hub in Workday.*
### *Insmed Incorporated is an Equal Opportunity employer. We do not discriminate in hiring on the basis of physical or mental disability, protected veteran status, or any other characteristic protected by federal, state, or local law. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.**It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.*
*Unsolicited resumes from agencies should not be forwarded to Insmed. Insmed will not be responsible for any fees arising from the use of resumes through this source. Insmed will only pay a fee to agencies if a formal agreement between Insmed and the agency has been established. The Human Resources department is responsible for all recruitment activities; please contact us directly to be considered for a formal agreement.*
*Insmed is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation to participate in the job application or interview process, please contact us by email at* *TotalRewards@insmed.com**and let us know the nature of your request and your contact information. Requests for accommodation will be considered on a case\-by\-case basis. Please note that only inquiries concerning a request for reasonable accommodation will be responded to from this email address.*
*Applications are accepted for 5 calendar days from the date posted or until the position is filled.**For New York City Residents:**To assist in identifying candidates with qualifications matching those required and/or preferred for this role, Insmed uses an Automated Employment Decision Tool (“AEDT”) that employs artificial intelligence to analyze and score information provided in resumes and application materials including, but not limited to, skills, work experience, education, and job\-related qualifications. The AEDT does not make final hiring decisions and all final hiring decisions are subject to human oversight and/or review.*
*If you are an applicant for this role and a New York City resident, you have the right to request: *A reasonable accommodation, if one is available under applicable law, by emailing* *TotalRewards@insmed.com* *; and/or***
- *An alternative selection process by emailing* *Privacy@insmed.com* *.*
- *Information about the type of data collected, the source of that data, and data retention practices related to the AEDT by emailing us at* *Privacy@insmed.com* *.*
Salary Context
This $255K-$347K range is above the 75th percentile 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 Insmed Incorporated, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($301K) sits 38% above the category median. Disclosed range: $255K to $347K.
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.
Insmed Incorporated AI Hiring
Insmed Incorporated has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bridgewater, NJ, US. Compensation range: $347K - $347K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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
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