Interested in this AI/ML Engineer role at Rosenxt Creation Center GmbH?
Apply Now →About This Role
To strengthen our development team we are looking for a
Engineering Manager Software & AI (all genders welcome)
We are a technology-driven company with several innovative business units in the growth phase. While we maintain certain centralized structures, we foster an agile, start-up-like spirit. Our teams develop advanced AI and software solutions that process large sensor datasets, build intelligent cloud services, and power real-time edge applications. We operate in domains where security, reliability, and engineering excellence are essential.
In this international environment, you will lead a multidisciplinary engineering team working mostly remotely across different US regions. This position is fully remote for candidates on the US East Coast, or fully remote/hybrid in Germany (with office options in Wietmarschen-Lohne, Osnabrück, or Bremen). This role requires travel to in-person workshops: approximately 2 per year in the US and 2–3 per year in Germany.
This role reports to the Head of Software and AI, with additional task management from the Head of AI, based in Europe, and partners closely with US branch management in California. It is designed for someone who thrives with a high degree of autonomy and wants to shape team operations while driving innovative product development.
What you can expect
- Lead and mentor a distributed US team of 13 engineers across AI/ML, cloud backend, frontend, and DevOps.
- Conduct regular 1:1s, manage performance, support career development, and foster a high-trust, continuous-learning team culture.
- Drive the delivery cadence by proactively clearing blockers, planning milestones, and ensuring reliable execution.
- Champion engineering excellence, including modern CI/CD, cloud automation, AI assistance, code reviews, monitoring, and documentation.
- Serve as the primary operational link between the US engineering team and the leadership organization in Germany.
- Partner closely with California branch management on HR, operational processes, and organizational alignment.
- Participate credibly in architectural discussions across AI/ML pipelines, cloud infrastructure, and core software systems.
- Identify delivery bottlenecks and implement lightweight processes that scale across international time zones.
What you bring
To become part of the Rosenxt family, you have a collaborative, empathetic and technically credible way of working. You should also bring:
- 7+ years in software engineering, with 2+ years managing cross-disciplinary technical teams (software, AI/ML, and DevOps).
- Strong technical background in at least one core area (AI/ML systems, cloud backend, or DevOps) with working fluency across the others.
- Proven track record of hiring, coaching, performance management, and building psychological safety in remote or distributed environments.
- Excellent cross-cultural communication and stakeholder management skills across executive levels, time zones, and global sites.
- Strong delivery management skills, including pragmatic prioritization, workload delegation, and trade-off decision-making.
- Bachelor’s degree in Computer Science, Software Engineering, AI/ML, or a related technical discipline.
- Production experience with AI/ML systems or high-reliability cloud and edge architectures (preferred).
- Experience in regulated or safety-critical industrial sectors, or familiarity with AI governance frameworks like the EU AI Act (preferred).
Look forward to
- Development opportunities and career opportunities in a global, innovative and long-term oriented group of companies with family character
- Flexible working time, working time accounts and Home Office possible
- An open, informal corporate culture, where we celebrate success with social events
- Depending on the hiring location you may also benefit from local benefits
More information about the Rosenxt Group please click here:
www.rosen-nxt.com
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Rosenxt Creation Center GmbH, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Rosenxt Creation Center GmbH AI Hiring
Rosenxt Creation Center GmbH has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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