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Bring more to life.
Are you ready to accelerate your potential and make a real difference within life sciences, diagnostics and biotechnology?
At SCIEX, one of Danaher’s 15\+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact.
You’ll thrive in a culture of belonging where you and your unique viewpoint matter. And by harnessing Danaher’s system of continuous improvement, you help turn ideas into impact – innovating at the speed of life.
As part of SCIEX, you will help influence life\-changing research and outcomes while accelerating your potential. For more than 50 years, we have been empowering our customers to solve the most impactful analytical challenges in quantitation and characterization through groundbreaking innovation and outstanding reliability and support. You will be part of a winning team, enabled by DBS, that is passionate about helping life science experts around the world get to answers they can trust.
Learn about the Danaher Business System which makes everything possible.
The Senior Director, AI Deployment \& Business Process Transformation is a strategic leadership role responsible for leading the enterprise AI agenda at SCIEX. This includes overseeing AI strategy development, transformation portfolio execution, and digital governance to deliver scalable, adopted solutions that generate quantifiable business outcomes.
This role leads the global integration of Danaher Business System (DBS) principles with AI capabilities to define and execute a scalable transformation strategy across SCIEX. By combining structured problem solving, standard work, governance, and continuous improvement with enterprise AI, this leader will identify high\-impact opportunities, accelerate deployment across core business processes, and ensure solutions are adopted in a disciplined and sustainable way. The Senior Director is accountable for translating strategy into measurable results while building the operating model, governance, and organizational capability required to embed AI at scale. Serving as a key liaison, this role aligns business stakeholders, Danaher, IT teams, and external partners to deploy AI solutions that are practical, seamlessly integrated into existing workflows, and maintained for long\-term effectiveness.
This position reports to the VP of Global Service, Operational Excellence and AI Deployment, is part of the Product Management group, and will be fully remote.
In this role, you will have the opportunity to:
- Co\-develop and own SCIEX's global multi\-year AI transformation strategy, ensuring alignment with Danaher priorities and SCIEX's broader growth agenda; leverage and activate Danaher's enterprise AI investments to accelerate impact at SCIEX, and represent the AI agenda in senior leadership forums with Danaher and key external partners.
- Identify, prioritize, and deploy high\-value AI use cases across service, commercial, operations, quality, and internal productivity \- translating business problems into clearly defined use cases with objectives, success metrics, adoption plans, and value realization measures to deliver measurable business impact.
- Partner with Danaher, IT, functional teams, and external vendors to design, pilot, scale, and sustain AI solutions across the enterprise \- bringing Danaher platform capabilities and proven solutions to life at SCIEX.
- Own end\-to\-end delivery of AI and digital initiatives \- including intake, charters, roadmaps, milestones, governance, risk management, and transition to sustainment \- applying DBS rigor to ensure on\-scope, on\-time, and on\-impact delivery.
- Develop productive partnerships with leading AI solution vendors in AI transformation. Lead cross\-functional, cross\-regional teams in a matrixed environment, balancing quality, execution, and change readiness across multiple concurrent initiatives; build and develop a high\-performing team of transformation professionals once success is proven.
- Own the SCIEX AI Steering Committee \- setting governance structure, managing the executive agenda, and driving accountability across member functions through consistent, business\-led decision\-making frameworks and operating rhythms.
- Drive structured change management, training, communications, and user feedback loops to embed AI solutions into daily workflows and improve adoption at scale; further build enterprise\-wide AI fluency through communities of practice, capability\-building programs, and post\-deployment ownership plans \- while serving as a trusted partner to functional and executive leadership with clear visibility to progress, risks, and outcomes.
The essential requirements of the job include:
- Bachelor’s degree in business, engineering, computer science, data science, life sciences, or a related field.
- Demonstrated experience leading complex, enterprise‑scale digital, AI, analytics, or business transformation programs, with direct accountability for delivery, adoption, and outcomes, and experience managing relationships with AI solution vendors.
- Experience deploying AI, automation, or advanced analytics use cases from pilot through scaled implementation in business or operational environments, in combination with structured business process improvement (DBS/Lean methodologies).
- Experience establishing program and portfolio governance, including roadmaps, milestones, risk management, and value tracking across multiple concurrent initiatives in a highly matrixed organization.
- Proficiency with program and portfolio management practices, including initiative planning, dependency management, performance reporting, and participation in executive governance forums.
- Experience leading enterprise‑scale, cross‑functional change deployment, including training, communications, rollout planning, and post‑deployment sustainment for new digital capabilities.
- Background working across commercial, service, operations, quality, or regulated business processes within life sciences, diagnostics, medtech, or similarly complex, regulated industries.
Travel, Motor Vehicle Record \& Physical/Environment Requirements:
- Willingness to travel up to 50%, including domestic and international travel per business needs.
- Valid driver’s license with an acceptable driving record and valid passport, or the ability to obtain and maintain both.
- Ability to perform the essential physical requirements of the role, including occasionally lifting or moving items up to 40 lbs., with or without reasonable accommodation, as needed.
It would be a plus if you also possess previous experience in:
- An advanced degree in business, engineering, computer science, data science, life sciences, or a related field.
- Experience deploying generative AI, machine learning, or intelligent automation solutions using enterprise platforms or cloud\-based AI environments.
- Experience supporting commercial, service, R\&D, operations, or quality functions in life sciences, diagnostics, biotechnology, or another regulated industry.
\#LI\-KW4
SCIEX, a Danaher operating company, offers a broad array of comprehensive, competitive benefit programs that add value to our lives. Whether it’s a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at Danaher Benefits Info.
At SCIEX we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, remote working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a remote work arrangement in which you can work remotely from your home. Additional information about this remote work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for SCIEX can provide.
The annual salary range for this role is $275,000 \- $300,000\. This is the range that we in good faith believe is the range of possible compensation for this role at the time of this posting. This range may be modified in the future.
This job is also eligible for bonus/incentive pay.
We offer a comprehensive package of benefits, including paid time off, medical/dental/vision insurance, and 401(k), to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Join our winning team today. Together, we’ll accelerate the real\-life impact of tomorrow’s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.
For more information, visit www.danaher.com.
Salary Context
This $275K-$300K 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 SCIEX, 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 ($287K) sits 31% above the category median. Disclosed range: $275K to $300K.
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.
SCIEX AI Hiring
SCIEX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Marlborough, MA, US. Compensation range: $300K - $300K.
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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