Interested in this AI/ML Engineer role at Merck KGaA?
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Work Your Magic with us! Start your next chapter and join MilliporeSigma.
Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet. That's why we are always looking for curious minds that see themselves imagining the unimaginable with us.
The preferred location for this role is Burlington, MA or St. Louis, MO. Other locations in the US, including remote, may also be considered.
Your Role
As the Product Specialist for Software \& AI, you will drive pioneering innovation in the strategy, development, and commercialization of cutting\-edge software and AI\-enabled solutions that revolutionize protein science workflows. Functioning as a technical product owner, this role requires deep expertise in scientific software and digital technologies, with a strong emphasis on transforming assay knowledge, customer insights, and automation data into smart, forward\-thinking, and intuitive product innovations that accelerate time\-to\-market.
You will collaborate closely with automation, R\&D, software engineering, and data science teams to co\-create advanced digital capabilities — owning the product vision and roadmap delivery — that set new standards for ease of use, reproducibility, and workflow efficiency across our Protein Sciences portfolio.
Key Responsibilities
Define and drive the technical roadmap for scientific software, data infrastructure, and AI/ML capabilities across the Protein Sciences portfolio, delivering differentiated data\-driven and algorithm\-enabled solutions.
Serve as the technical and innovation lead for scientific software and AI, bridging assay workflows, data models, and advanced analytics to enable next\-generation protein analysis.
Identify and drive opportunities where AI and modern data methods unlock new capabilities, advancing assay development, signal processing, data harmonization, and performance monitoring beyond traditional approaches.
Translate customer, market, and workflow needs into software and AI product requirements, including data models, feature definitions, and user experiences aligned to real\-world scientific applications.
Lead the integration of software, data, and automation systems, defining data capture, metadata standards, control interfaces, and interoperability across instruments and digital platforms.
Drive cross\-functional product development and innovation, partnering with Software Engineering, UX, R\&D, Automation, and PM Operations — managing the full product development lifecycle from concept through launch to ensure on\-time, on\-strategy delivery.
Evaluate and develop strategic partnerships with external software, data, and AI providers to accelerate platform capabilities and innovation.
Support go\-to\-market strategy and commercial enablement, applying core product management principles including product positioning, voice\-of\-customer synthesis, messaging, and launch execution for software and AI solutions.
Minimum Qualifications
- Bachelor’s degree in biology, bioengineering, computer science, data science, or related field.
- 5\+ years of experience with scientific software, digital platforms, or data/analytics solutions in life science research.
Preferred Qualifications
- M.Sc., Ph.D., or MBA.
- Experience with AI/ML applications in assay development, signal analysis, data interpretation, or workflow optimization.
- Familiarity with cloud‑based software, data pipelines, and workflow orchestration tools.
- Strong ability to translate scientific complexity into user‑friendly digital tools.
- Experience working across biology, software, engineering, and commercial teams; familiarity with agile product management methodologies and cross\-functional roadmap execution is a plus.
*US Pay Range for this position: $116,600\-$175,000*
*The offer range represents the anticipated low and high end of the base pay compensation for this position. The actual compensation offered will be determined by factors such as location, level of experience, education, skills, and other job\-related factors. Position may be eligible for sales or performance\-based bonuses. Benefits offered by the Company include health insurance, paid time off (PTO), retirement contributions, and other perquisites. For more information click* *here**.*
What we offer: We are curious minds that come from a broad range of backgrounds, perspectives, and life experiences. We believe that this variety drives excellence and innovation, strengthening our ability to lead in science and technology. We are committed to creating access and opportunities for all to develop and grow at your own pace. Join us in building a culture of inclusion and belonging that impacts millions and empowers everyone to work their magic and champion human progress!
Apply now and become a part of a team that is dedicated to Sparking Discovery and Elevating Humanity!
Salary Context
This $116K-$175K 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 Merck KGaA, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $116K to $175K.
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.
Merck KGaA AI Hiring
Merck KGaA has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Burlington, MA, US, Rockville, MD, US. Compensation range: $142K - $175K.
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
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