Interested in this AI/ML Engineer role at Siemens?
Apply Now →About This Role
Siemens EDA is a global technology leader in Electronic Design Automation software. Our software tools enable companies around the world to develop highly innovative electronic products faster and more cost\-effectively. Our customers use our tools to push the boundaries of technology and physics to deliver better products in the increasingly complex world of chip, board, and system design.
We are looking for a proven technology leader to build and lead a new effort focused on one of the biggest opportunities in semiconductor design software: AI\-driven design migration.
This is not a maintenance role. This is a chance to define and deliver technology that helps customers move complex analog, custom IC, photonics, and MEMS designs between technologies, design environments, and process nodes with far less manual effort. The work sits at the intersection of AI, automation, design intelligence, and semiconductor design.
We are looking for someone who has built products, led teams, and delivered results. Someone who has a track record of turning ambitious ideas into production software that customers depend on. Someone recognized as an innovator by peers, customers, and industry partners.
Why This Role Matters
Design migration is one of the largest unsolved productivity problems in chip design.
Customers spend enormous amounts of engineering effort porting designs between technologies, foundries, process nodes, and design environments. We believe AI can dramatically reduce that effort and create a new generation of intelligent migration solutions.
As Director of AI\-Driven Design Migration, you will define the strategy, build the team, and lead the execution of this vision. Success in this role has the potential to create significant customer value and become a major growth driver for the business.
Responsibilities
- Define and lead the product and technology strategy for AI\-driven design migration.
- Build and lead a high\-performing team of software engineers, architects, and technical experts.
- Drive delivery from concept through customer deployment.
- Develop technologies that automate migration of analog, custom IC, photonics, and MEMS designs.
- Apply AI, machine learning, generative AI, and agent\-based approaches to solve complex migration challenges.
- Work directly with customers to understand problems, validate solutions, and guide product direction.
- Partner with product management, engineering, and business leadership to establish priorities and investment plans.
- Recruit, mentor, and develop technical leaders and future leaders within the organization.
- Establish technical direction while maintaining accountability for execution, quality, and customer success.
- Represent the technology internally and externally with customers, partners, and industry stakeholders.
Requirements
- BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, Physics, or a related technical field.
- Significant experience delivering commercial software products.
- Experience leading engineering teams and technical programs.
- Proven track record of bringing new technologies, products, or major capabilities to market.
- Strong software engineering background, including C\+\+ and modern software development practices.
- Experience applying AI, machine learning, generative AI, or agentic AI technologies to real\-world problems.
- Strong background in algorithms, software architecture, and large\-scale system design.
- Ability to translate vision into execution and execution into customer value.
- Strong communication skills and executive presence.
- Demonstrated success working directly with customers and influencing product direction.
Preferred Experience
- Semiconductor design software (EDA).
- Analog, custom IC, photonics, MEMS, or physical design workflows.
- OpenAccess\-based design environments and commercial design platforms.
- Design migration, design reuse, process migration, or technology porting solutions.
- Layout automation, placement, routing, verification, or design optimization technologies.
- Graph algorithms, optimization, geometric computing, or design intelligence systems.
- Building and scaling global engineering teams.
- Experience leading organizations through periods of significant growth, innovation, or transformation.
Who Will Be Successful
The ideal candidate is recognized as a technical leader and innovator. They have led teams, delivered products, and built technology that created measurable business impact. They know how to attract strong talent, make difficult decisions, and maintain focus on outcomes.
They are equally comfortable discussing long\-term strategy with executives, diving into technical details with engineers, and solving real customer problems.
Most importantly, they have a history of turning ambitious ideas into products that customers actually use.
The Opportunity
This role offers the opportunity to define a major new technology area at the intersection of AI and semiconductor design. The successful candidate will have the mandate, visibility, and resources to build something significant, while working on problems that few companies in the world are positioned to solve
This position will be subject to U.S. export control requirements under the International Traffic in Arms Regulations (ITAR) and/or Export Administration Regulations (EAR). Employment is contingent on either verifying the U.S. Person status or obtaining any necessary export license.
Why us?
Working at Siemens Software means flexibility \- Choosing between working at home and the office at other times is the norm here. We offer great benefits and rewards, as you'd expect from a world leader in industrial software.
A collection of over 377,000 minds building the future, one day at a time in over 200 countries. We're dedicated to equality, and we welcome applications that reflect the diversity of the communities we work in. All employment decisions at Siemens are based on qualifications, merit, and business need. Bring your curiosity and creativity and help us shape tomorrow!
Siemens believes in fostering a work environment that promotes a healthy work\-life balance. Our world class benefits package includes 401k matching, stock purchase plan, annual performance reviews/bonuses, education reimbursement, partially paid Medical/Dental/Vision insurance, Life, Short/Long Term Disability, and a generous time off plan. https://benefits\-jobs\-sw\-siemens\-com.microsites.recruitrooster.com/benefits/
The salary range for this position is $213,700\.00 to $341,900\.00 and this role is eligible to earn incentive compensation. The actual compensation offered is based on the successful candidate’s work location as well as additional factors, including job\-related skills, experience, and relevant education/training. Siemens offers a variety of health and wellness benefits to employees. Details regarding our benefits can be found here: www.benefitsquickstart.com. In addition, this position is eligible for time off in accordance with Company policies, including paid sick leave, paid parental leave, PTO (for non\-exempt employees) or non\-accrued flexible vacation (for exempt employees)
Siemens Software. *Transform the Everyday*
\#LI\-EDA
\#LI\-HYBRID
\#LI\-AJ1
$180,400 $324,700 15 \- 25%
Salary Context
This $213K-$341K range is above the 75th percentile 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 Siemens, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($277K) sits 27% above the category median. Disclosed range: $213K to $341K.
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.
Siemens AI Hiring
Siemens has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span New York, NY, US, Santa Clara, CA, US, Seattle, WA, US. Compensation range: $158K - $341K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.