Interested in this AI/ML Engineer role at Danaher?
Apply Now →Skills & Technologies
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.
Learn about the Danaher Business System which makes everything possible.
The Sr. AI Engineer, Device Intelligence will be a key member of the AI Product and Imaging Innovation team, reporting to its Sr Director. This new role is instrumental in the implementation of cutting\-edge AI systems that leverage data created by Danaher devices to extract meaningful insights and dramatically improve user experience, with the goal of upleveling Danaher's devices across Life Sciences, Diagnostics and Biotechnology sectors. This position is remote in Eastern US.
In this role, you will have the opportunity to:
- Collaborate with the VP and the broader team to develop Enterprise grade capabilities and platforms enable the creation of intelligent instruments and autonomous labs.
- Design and implement advanced AI systems, including next\-generation multi\-level and multi\-domain agentic systems, to enhance instrument intelligence and lab automation, upleveling user capabilities.
- Work closely with cross\-functional and cross company teams to translate product concepts into concrete AI architectures, ensuring alignment with product success criteria and measurable KPIs.
- Lead the technical implementation of AI projects of notable complexity, involving multiple functions and typically spanning multi\-quarter timelines.
- Drive innovation in the AI software stack, focusing on maximizing value extraction from instruments for customers through AI systems. Collaborate with business stakeholders, technical experts, and subject matter experts to build comprehensive AI roadmaps for imaging products across various verticals.
The essential requirements of the job include:
- Bachelor's degree in computer science, electrical engineering, robotics, automation, life sciences or a related field. An advanced degree (MS or PhD) in a relevant area (i.e. Neural Networks, Foundation Model, Agentic Workflows, Machine Learning, Computer Vision, Robotics) is preferred.
- Long standing experience in the Life Science, Diagnostics, Biotechnology or Tech Industry, including multiple years of experience with Agentic Systems, LLMs, Automation, Robotics, and/or Multimodal AI. Demonstrable expertise with at least 3 of the following: convolutional neural networks, foundation models, vector databases, AI evaluation methods, RAG approaches, agentic frameworks, robotics.
- Proven track record of successful end\-to\-end implementation of AI projects in imaging applications, particularly in life sciences or healthcare domains.
- Strong programming skills in languages such as Python or C\+\+, and extensive experience with deep learning frameworks (e.g., TensorFlow, PyTorch, OpenCV) and containerization technologies (e.g., Docker, Kubernetes). Including the use of genAI and agentic systems for coding. Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and familiarity with hardware acceleration technologies (e.g. TPUs, GPUs, ARM\-CPUs).
- Demonstrated ability to work effectively with cross\-functional teams, communicate complex technical concepts to both technical and non\-technical stakeholders, and align AI development with product goals and roadmaps.
It would be a plus if you also possess previous experience in:
- Experience with regulatory processes, especially for medical devices and AI/ML\-based software as a medical device (SaMD).
- Familiarity with quality management systems and standards relevant to the life sciences and diagnostics industries.
- Knowledge of instrument control mechanisms and how they integrate with AI systems for enhanced automation.
- 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.
- At Danaher, 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 Danaher can provide.
The annual salary range for this role is $135,000 \- $180,000*.* 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 $135K-$180K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 27% below the category median. Disclosed range: $135K to $180K.
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.
Danaher AI Hiring
Danaher has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $180K - $180K.
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.