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About This Role
Bring more to life.
At Danaher, our work saves lives. And each of us plays a part. Fueled by our culture of continuous improvement, we turn ideas into impact – innovating at the speed of life.
Our 60,000+ associates work across the globe at more than 15 unique businesses within life sciences, diagnostics, and biotechnology.
Are you ready to accelerate your potential and make a real difference? At Danaher, you can build an incredible career at a leading science and technology company, where we’re committed to hiring and developing from within. You’ll thrive in a culture of belonging where you and your unique viewpoint matter.
DHR Enterprise AI Solutions is Danaher's enterprise AI engineering capability, responsible for identifying opportunities, building and scaling reusable AI solutions, and accelerating measurable business impact across operating companies. We combine product, architecture, AI science, engineering and operations to industrialize AI, establish enterprise standards and enable the next generation of AI-powered products, workflows, applications and decision-making. The Lead AI Scientist must understand AI algorithms at a scientific level, including their development, progression, and interaction with data. The Lead AI Scientist must be highly proficient in Python and capable of performing hands-on technical work rather than operating primarily as a people manager. Candidates should have held at least a senior or lead position, although a technically hands-on director from a startup could also be considered. The role may mentor junior AI engineers and scientists but will not have direct reports.
We look forward to welcoming you to our organization. In DHR Enterprise AI Solutions, you will be responsible for exploring and validating emerging AI methods, designing rigorous evaluations and turning promising research into practical capabilities that Danaher can industrialize and scale. This hybrid role will require working occasionally in our Boston facility.
In this role, you will have the opportunity to:
- Identify and investigate emerging AI methods with potential to address high-value scientific, operational or commercial challenges across Danaher.
- Design rigorous experiments, benchmarks and evaluation approaches that make capability, limitations and risk transparent.
- Develop and test novel modelling, reasoning, retrieval or agentic approaches in partnership with AI and software engineers.
- Translate research findings into clear recommendations, reusable methods and production-minded prototypes for wider adoption.
- Communicate complex scientific concepts to technical and business audiences and mentor colleagues in advanced AI practices.
The essential requirements of the job include:
- Minimum master’s degree, with a PhD preferred. Relevant disciplines include artificial intelligence, data science, machine learning, computer science, mathematics, physics, or a related subject. Candidates without a PhD should have approximately five years of experience following completion of a master’s degree. PhD work may satisfy the experience expectation.
- Strong expertise in machine learning, generative AI, statistical methods and experimental design.
- Ability to move from literature and emerging research to reproducible experiments and practical enterprise applications.
- Hands-on proficiency with Python, modern AI frameworks, evaluation methods and relevant cloud or compute environments.
- Sound scientific judgement, intellectual curiosity and the ability to challenge assumptions with evidence.
- Excellent written and verbal communication skills across research, engineering and business audiences.
- Ability to travel up to 10% domestically and internationally, as needed, to support program delivery, business engagement, partner coordination, audits, training, or operational readiness.
It would be a plus if you also possess previous experience in:
- Applied AI research or advanced model-development programs.
- Designing model, agent or system evaluation frameworks.
- Transferring research innovations into enterprise products or production solutions.
Danaher 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.
The annual salary range for this role is $175,000 - $200,000. 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 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 $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Danaher, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Danaher AI Hiring
Danaher has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Boston, MA, US, Cracow, Małopolskie, Poland, Washington, DC. Compensation range: $200K - $200K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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