Interested in this AI/ML Engineer role at Merck KGaA?
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About This Role
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.
Location: San Jose, California \| Remote Flexibility Available
Your Role
You will be the technical cornerstone of a high\-impact, multi\-agent platform that powers AI\-amplified research and development. In this pivotal role, you will own the architecture and evolution of a production\-grade agentic system that enables autonomous experiment design and automated reporting at scale. Your work will directly shape how science and technology interact, driving meaningful progress for patients and customers worldwide.
You will define and uphold engineering standards through hands\-on code review, pairing, and concrete examples, ensuring quality and maintainability endure beyond individual contributors. You will build and maintain shared agentic infrastructure that allows multiple programs to contribute specialized agents within a single governed platform, connecting it to external tooling, partners, and the broader ecosystem for seamless integration and scalable growth.
You will own platform design decisions spanning data sources, access controls, agent composition patterns, scalability, and technical debt management. Within a SAFe delivery cadence, you will lead the technical roadmap, guiding both internal engineers and external contractors to maintain architectural consistency. You will ship production code, own CI/CD pipelines and observability, and drive robust testing and security practices for sensitive research data.
You will translate complex research and development requirements into reliable technical solutions and communicate them clearly to both technical and non\-technical stakeholders. You will mentor developers, lead by example, and help the team navigate the evolving AI landscape with disciplined, evidence\-based decision making. As the permanent internal anchor for architecture and standards, you will ensure the platform remains reliable, secure, and extensible as the program scales.
Who You Are
Minimum Qualifications:
- Master’s degree in computer science, data science, chemistry, materials science, or a related field with 3\+ years of experience Or Ph.D in computer science, data science, chemistry, materials science, or a related field with 2\+ years of experience.
- Demonstrated experience in technical leadership, including making architectural decisions under uncertainty and guiding cross\-functional engineering teams.
- Solid understanding of distributed systems, APIs, and cloud infrastructure on AWS and/or Azure.
- Security\-first mindset with hands\-on knowledge of authentication, authorization, input validation, and AI\-specific risks such as prompt injection and data leakage.
- Experience establishing engineering standards, automated testing strategies, and maintainable code practices that scale across teams and contributors.
Preferred Qualifications:
- Proven track record of building and operating complex, production\-grade software systems end to end, including ownership of CI/CD, observability, and on\-call operations.
- Experience designing or operating multi\-agent or agentic AI platforms in a production environment.
- Familiarity with large language model (LLM) orchestration frameworks and patterns for agent composition and governance.
- Experience working within SAFe or similar scaled agile delivery frameworks.
- Strong communication skills with the ability to bridge software engineering and non\-technical research or product stakeholders.
- Familiarity with research and development workflows, scientific data environments, or regulated data contexts where security and data governance are critical.
*Base Pay Range for this position: $119,300\-$178,900*
*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 $119K-$178K range is below 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 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 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 ($149K) sits 32% below the category median. Disclosed range: $119K to $178K.
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.
Merck KGaA AI Hiring
Merck KGaA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $178K - $178K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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
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