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About This Role
Join us in pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
Our inspiring and caring environment forms a global community that celebrates diversity and individuality. We encourage you to step beyond your comfort zone, offering resources and flexibility to foster your professional and personal growth, all while valuing your unique contributions.
Lead the Intelligence Logic team responsible for the next generation of endovascular robotic interventions, spanning real-time image analytics for device navigation and computer vision, and procedural workflow orchestration that models, predicts, and automates (neuro)endovascular procedures.
Your tasks and responsibilities:
- You lead and mentor a team of software developers spanning computer vision/AI imaging (Software Developer CV) and workflow orchestration (Software Developer WfO), setting technical direction and ensuring alignment across both disciplines.
- You coordinate and shape the technical roadmap for the integration of AI-driven image analytics, sensor fusion/localization, and workflow engines that coordinate imaging and robotics in the interventional suite, working in close alignment with and support development teams in Germany.
- You oversee the development and evaluation of deep learning approaches for real-time image analysis (segmentation, registration, object detection/tracking) as well as state machines and digital representations of (neurovascular) procedures.
- You partner closely with cross-functional experts, including computer vision, robotics, clinical, and hardware teams, to ensure safe orchestration of clinical workflows, including failure detection and uncertainty estimation.
- You manage team priorities, sprint planning, and delivery milestones, removing blockers and ensuring the team follows DevOps and MLOps best practices spanning the full lifecycle of software and machine learning models.
- You work with multi-modal data relevant to minimally invasive therapies such as x-ray fluoroscopy, digital subtraction angiography, (cone-beam) computed tomography, robotic data and endovascular device shape sensing.
- You work in an interdisciplinary regulated medical device environment and collaborate closely with cross-functional experts.
To find out more about the specific business, have a look at https://www.siemens-healthineers.com/products-services
Your qualifications and experience:
- You have a Master’s degree with 6+ years of experience in Biomedical Engineering, Computer Science, Artificial Intelligence, Robotics, or a related field. A PhD is highly preferred.
- You have prior experience leading or mentoring engineers and/or software developers, e.g., as a technical lead, team lead, or in a similar people-management capacity within a software or R&D organization.
- You have a strong background in computer vision and medical image analysis including common tasks such as segmentation, registration, and object detection and tracking.
- You have an in-depth understanding of state-of-the-art deep learning methods.
- You have a strong background of robotics concepts including state estimation, sensor fusion and working knowledge of control theory concepts like model predictive control
- You have experience in development of machine learning models using modern frameworks such as PyTorch and MLOps approaches spanning the whole lifecycle of machine learning models.
- You have experience in programming with Python and C++/Cuda, and software engineering following DevOps best practices.
Scientific publications in relevant top-tier journals or venues such as IEEE TMI, CVPR, ICCV/ECCV, MICCAI, or comparable are a plus. *
Your attributes and skills:
- You have a very good command of English, both written and spoken, which enables you to communicate effectively in our international environment.
- You demonstrate strong leadership, coaching, and communication skills, with the ability to motivate a technically diverse team and align it around shared goals.
- You are creative and continuously develop innovative ideas to further develop our solutions in the field of image-guided automation.
- On a personal level, you impress with your organizational skills and communication abilities, initiative, and curiosity to learn.
Who we are: We are a team of more than 72,000 highly dedicated Healthineers in more than 70 countries. As a leader in medical technology, we constantly push the boundaries to create better outcomes and experiences for patients, no matter where they live or what health issues they are facing. Our portfolio is crucial for clinical decision-making and treatment pathways.
How we work: When you join Siemens Healthineers, you become one in a global team of scientists, clinicians, developers, researchers, professionals, and skilled specialists, who believe in each individual’s potential to contribute with diverse ideas. We are from different backgrounds, cultures, religions, political and/or sexual orientations, and work together, to fight the world’s most threatening diseases and enable access to care, united by one purpose: to pioneer breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
To find out more about Siemens Healthineers businesses, please visit our company page here.
The base pay range for this position is:
$114,920 - $158,015 Factors which may affect starting pay within this range may include geography/market, skills, education, experience, and other qualifications of the successful candidate.
If this is a commission eligible position the commission eligibility will be in accordance with the terms of the Company's plan. Commissions are based on individual performance and/or company performance.
The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan. life insurance, long-term and short-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time.
Equal Employment Opportunity Statement: Siemens Healthineers is an Equal Opportunity and Affirmative Action Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law.
EEO is the Law: Applicants and employees are protected under Federal law from discrimination. To learn more, click here.
Reasonable Accommodations: Siemens Healthineers is committed to equal employment opportunity. As part of this commitment, we will ensure that persons with disabilities are provided reasonable accommodations.
If you require a reasonable accommodation in completing a job application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please fill out the accommodations form here. If you’re unable to complete the form, you can reach out to our HR People Connect People Contact Center for support at [email protected]. Please note HR People Connect People Contact Center will not have visibility of your application or interview status.
California Privacy Notice: California residents have the right to receive additional notices about their personal information. To learn more, click here.
Export Control: “A successful candidate must be able to work with controlled technology in accordance with US export control law.” “It is Siemens Healthineers’ policy to comply fully and completely with all United States export control laws and regulations, including those implemented by the Department of Commerce through the Export Administration Regulations (EAR), by the Department of State through the International Traffic in Arms Regulations (ITAR), and by the Treasury Department through the Office of Foreign Assets Control (OFAC) sanctions regulations.”
Data Privacy: We care about your data privacy and take compliance with GDPR as well as other data protection legislation seriously. For this reason, we ask you not to send us your CV or resume by email. We ask instead that you create a profile in our talent community where you can upload your CV. Setting up a profile lets us know you are interested in career opportunities with us and makes it easy for us to send you an alert when relevant positions become open. Register here to get started.
Beware of Job Scams: Please beware of potentially fraudulent job postings or suspicious recruiting activity by persons that are currently posing as Siemens Healthineers recruiters/employees. These scammers may attempt to collect your confidential personal or financial information. If you are concerned that an offer of employment with Siemens Healthineers might be a scam or that the recruiter is not legitimate, please verify by searching for the posting on the Siemens Healthineers career site.
To all recruitment agencies: Siemens Healthineers does not accept agency resumes. Please do not forward resumes to our jobs alias, employees, or any other company location. Siemens Healthineers is not responsible for any fees related to unsolicited resumes.
Salary Context
This $114K-$158K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Siemens Healthineers, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($136K) sits 36% below the category median. Disclosed range: $114K to $158K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Siemens Healthineers AI Hiring
Siemens Healthineers has 3 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Newton, MA, US, Princeton, NJ. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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