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 EMD Electronics.
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.
Everything we do in EMD Electronics is to help us deliver on our purpose of being the company behind the companies, advancing digital living. We are dedicated to being the trusted supplier of high\-tech materials, services and specialty chemicals for the electronics, automotive and cosmetics industries. We foster a global collaborative organization made up of individuals who have the passion to win, obsess about the customer, are relentlessly curious and act with urgency. Together, we push the boundaries of science to make more possible for our customers.
Your Role:
The materials discovery cycle is being fundamentally reshaped by AI from how experiments are designed and hypotheses generated, to how results are analyzed and learning is accumulated across thousands of runs. This role owns the AI and discovery half of that transformation at EMD Electronics, leading the programs that put intelligent systems directly in the hands of R\&D scientists.
As part of the wider Materials AI portfolio and team, you will inherit live programs with real users and scientists already in motion, and a portfolio that has already demonstrated measurable scientific impact. The mandate is to deepen that impact, extend the AI stack to new material systems and domains, and shape what AI\-enabled discovery looks like at EMD Electronics.
Strategic Scope \& Impact
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- Portfolio of AI\-enabled discovery programs spanning co\-scientist agents, Bayesian optimization, and sequential learning — deployed across global R\&D and expanding to new material systems
- Leadership across programs in Germany and the US, embedded within the broader Materials AI organization
- External partnership agenda: Identifying, evaluating, and structuring collaborations with AI startups, technology providers, and academic groups to bring frontier capabilities into EMD R\&D ecosystem
- Long\-term AI\-for\-science vision: generative models and autonomous experimentation connecting EMD Electronics' capabilities to the businesses of Merck KGaA, Darmstadt, Germany (MilliporeSigma and EMD Serono).
Key Responsibilities
Strategy \& Portfolio
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Define and evolve the AI\-enabled discovery roadmap, aligned to the Connected* Amplified R\&D phases
- Own and prioritize the AI discovery program portfolio; manage budget, delivery milestones, and TLB reporting
- Translate technology advances in AI and scientific ML into a sequenced, fundable program agenda
Leadership
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- Foster tight collaboration with the computational modelling and lab digitization sub\-teams within Materials AI
- Develop program leads toward greater ownership and scientific ambition
Technical \& Scientific Delivery
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- Scale a deployed multi\-agent AI co\-scientist from internal platform toward the primary R\&D workflow tool for experiment design, documentation, and analysis
- Extend Bayesian optimization capabilities to new material systems and R\&D domains; deepen sequential learning workflows and instrument data integration
- Identify and pilot emerging AI approaches, e.g., generative models, autonomous experimentation, where they create genuine scientific leverage
Partnerships \& External Engagement
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- Build and manage collaborations with AI startups, academic groups, and technology providers; structure engagements that bring frontier capabilities in\-house
- Represent EMD Electronics' AI\-for\-science work externally at conferences and in strategic partnerships
- Connect relevant initiatives across Merck KGaA, Darmstadt, Germany
Who You Are
Minimum Qualifications:
- Advanced degree in computer science, data science, chemistry, materials science, or a related field
- 5\+ years in technical leadership or program management in an AI/ML context within chemical R\&D
- Hands\-on experience with AI/ML i.e. LLMs, Bayesian optimization, or scientific ML in a real deployment context
- Demonstrated track record of building and managing collaborations with external partners, including startups, academic groups, or technology providers.
Preferred Qualifications
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- PhD in relevant scientific or technical discipline.
- Proficiency in agentic AI architectures, LLM\-based systems, and modern machine learning frameworks.
- Familiarity with Bayesian optimization, sequential learning, or computational materials science.
- Background in semiconductor or specialty chemicals R\&D; understanding of R\&D workflows and scientific data acquired through hands\-on laboratory experience.
Location: San Jose California, United States
*Base Pay Range for this position – $200,200\-$300,400*
*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 $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($250K) sits 35% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Merck KGaA AI Hiring
Merck KGaA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Darmstadt, IN, US, San Jose, CA, US. Compensation range: $300K - $340K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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