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 Senior Legal Engineer is responsible for designing, building, and deploying AI\-powered solutions that transform legal and compliance workflows across Danaher's corporate and platform functions and operating companies. This role sits at the intersection of software engineering, data science, and legal operations — applying deep technical expertise in large language models (LLMs), agentic AI, and modern AI infrastructure to solve high\-impact legal problems at enterprise scale.
In this role, you will have the opportunity to:
- Partner with legal stakeholders to translate workflows into technical solutions — Conduct discovery with attorneys and compliance professionals across Danaher to understand their workflows, identify automation opportunities, and deliver solutions that legal teams trust and adopt.
- Architect and build AI\-powered legal applications. Design end\-to\-end solutions for contract review, legal research, regulatory analysis, and compliance workflows using LLMs, retrieval\-augmented generation (RAG), and agentic AI frameworks. Develop and optimize document processing pipelines. Build scalable ingestion, parsing, chunking, embedding, and indexing systems for legal content (contracts, playbooks, regulatory filings) to power intelligent search and analysis.
- Ship production\-grade AI features. Write clean, well\-tested code (Python and/or TypeScript) to deliver production applications using LLM APIs, orchestration frameworks (e.g., LangChain, LlamaIndex), and CI/CD pipelines — from prototype through deployment and monitoring. Design evaluation and quality framework. Build automated evaluation pipelines, test sets, and quality metrics specific to legal AI outputs, ensuring accuracy, consistency, and defensibility of AI\-generated work product.
- Embed governance and security by design — Implement data security controls, access management, audit logging, PII redaction, and data residency compliance appropriate for applications handling privileged and confidential legal content. Apply product thinking to legal AI solutions — Understand user needs through discovery and feedback, translate them into clear product and technical requirements, and help prioritize features that drive adoption and measurable business value.
- Technical leadership and mentorship \- Lead, educate and mentor legal team members in developing no\-code AI solutions. Serve as the technical bridge between legal and engineering. Communicate clearly across disciplines, deliver demos and training to legal users, and channel user insights back into product development and roadmap priorities. Partner closely with IT, Data \& AI teams to design, build, and deliver scalable AI solutions aligned with enterprise architecture and governance standards.
The essential requirements of the job include:
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field (Master's or advanced degree is a plus). 5\+ years of professional experience in software engineering, data science, or machine learning engineering, with a track record of shipping production applications.
- Production experience with large language models (LLMs), including prompt engineering, fine\-tuning, RAG architecture, agent/tool\-use development, and evaluation frameworks. Strong programming proficiency in Python, with experience in AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, Hugging Face, or equivalent orchestration tools); proficiency in an additional language (TypeScript, Java) is a plus.
- AI‑Assisted Software Development – Demonstrated proficiency leveraging AI development tools (e.g., Claude Code, Cursor, GitHub Copilot) to accelerate software solution development in production environments. Hands\-on experience with modern AI infrastructure, including vector databases (e.g., Pinecone, Weaviate, Qdrant), cloud platforms (Azure, AWS, or GCP), API design, and containerized deployment (Docker, Kubernetes).
- Experience designing data pipelines and document processing systems for unstructured content at scale (parsing, OCR, entity extraction, classification).
- Demonstrated interest or experience in legal, compliance, or regulatory domains — e.g., prior work in legal tech, contract analytics, regulatory technology, legal operations, paralegal experience, law school coursework, or JD.
It would be a plus if you also possess previous experience in:
- Building or deploying AI solutions in regulated industries (life sciences, healthcare, financial services, legal).
- Agentic AI patterns — multi\-step autonomous workflows with human\-in\-the\-loop oversight, tool calling, MCP servers, and structured outputs.
- Enterprise AI governance and responsible AI practices, including model evaluation, bias testing, explainability, and compliance with frameworks such as the EU AI Act.
Travel, Motor Vehicle Record \& Physical/Environment Requirements:
- 10\-25% travel for meetings and conferences
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 salary range for this role is $185,000\-$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 a comprehensive package of benefits including paid time off, medical/dental/vision insurance, and 401(k) to eligible employees.
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 $185K-$200K range is above the median 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $185K to $200K.
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.
Danaher AI Hiring
Danaher has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $220K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.