Interested in this AI/ML Engineer role at SPARETECH GmbH?
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Your Mission
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As a Senior Marketing AI Manager, you will be responsible for building and running the AI operating layer in the Marketing Team. Your role, as a senior horizontal enabler, will be to make Product Marketing, Brand Marketing, and Integrated Marketing significantly more productive by turning applicable work into governed AI workflows, reusable agents, and shared systems.
We offer you the opportunity to
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- Marketing AI operating model: Define how AI is used across the marketing organization, including approved tools, role\-based use cases, prompt and evaluation standards, human\-review rules, and lightweight governance for accuracy, brand consistency, and responsible data handling.
- Workflow and agent development: Design, build, test, and improve reusable AI workflows and agents for high\-value marketing jobs such as competitive battle cards, launch\-plan builders, sales\-enablement copilots, pitch\-deck builders, win\-loss synthesis, brand\-guardrail workflows, content brief generation, email drafting, social monitoring, visualization support, and other recurring tasks.
- Environment and systems setup: Create the working environment that makes those workflows usable in practice by connecting the right tools, data sources, templates, and automations across the marketing stack. Partner with RevOps, IT, and Security where integrations, access controls, or vendor review are required.
- Adoption and enablement: Train the marketing team through onboarding, workshops, quick\-start guides, templates, documentation and coaching. Build repeatable skills, not just one\-time excitement.
- Intake and prioritization: Run a pragmatic backlog of AI initiatives and requests, prioritizing the highest\-frequency, highest\-friction, highest\-impact use cases across the team.
- Measurement and iteration: Track adoption, time saved, cycle\-time reduction, usage, output quality, and pipeline\-adjacent impact. Continuously improve workflows based on team feedback and business results.
- Continued Learning: Stay up to date with emerging AI tools, trends, and best practices, and continuously translate them into practical applications for the team
Become a part of our team if you
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- 6\+ years of experience in B2B marketing, ideally in enterprise SaaS.
- Strong marketing fundamentals across positioning, messaging, sales enablement, launches, content workflows, and pipeline generation.
- A meaningful background in Product Marketing, Marketing Operations, or a role that combines both strategic marketing judgment and systems thinking.
- Hands\-on experience using AI in real marketing workflows, not just experimenting with chat tools.
- Experience designing repeatable systems, templates, workflows, or internal tooling that improve team productivity and output quality.
- Comfort building with low\-code and no\-code tools, workflow automation, and AI assistants or agents; bonus points for API fluency, SQL, or light scripting.
- Strong stakeholder management skills and the ability to work across functions without taking over their execution.
- Great communication and training skills, with the ability to make technical concepts understandable and practical for non\-technical teammates.
- A structured, pragmatic mindset with a bias toward measurable impact.
- Comfort operating in a lean, fast\-moving startup environment.
Nice to have
- Experience in industrial, manufacturing, asset management, CMMS, maintenance, procurement, or other operationally complex B2B categories.
- Experience working with global enterprise buyers and long sales cycles.
- Familiarity with AI governance, evaluation methods, content QA, and vendor assessment.
- Experience supporting CRM, marketing automation, enrichment, or analytics workflows in partnership with RevOps or Marketing Ops.
What is outside the scope of this role
- This role is not the owner of all campaign execution or content production.
- This role is not the company\-wide owner of AI governance, security policy, or legal approvals.
- This role is not the catch\-all owner for every routine marketing ops request.
- This role is not measured by the number of demos or tools shipped alone. It is measured by adoption, efficiency, quality, and business impact.
You can Expect
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Our base salary is just one component of the competitive total rewards strategy. As an organization, one of our top priorities is to maintain the health and well\-being of our employees. To achieve this goal, we offer robust and comprehensive benefits including:
- Paid Time Off: 20 days of vacation, plus public holidays.
- Hybrid Work Model: Enjoy a flexible work setup, typically spending two days per week in our Chicago office.
- Professional Development: An annual training budget of $1,000 to support your growth in a dynamic, startup environment.
- Ownership Mindset: Participate in our VSOP program and contribute directly to the growth and success of a fast\-scaling company.
- Healthcare Coverage: Healthcare benefits to help support you and your family.
- Make a Visible Impact: Be part of a team where your ideas matter and where you'll help build the foundations, processes, and culture that support our continued growth in North America.
*Compensation is determined based on several factors such as the candidate's professional history, experience, and business objectives. Actual offer amounts may differ from the figures provided below.*
About SPARETECH
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SPARETECH's vision is to empower the zero\-waste industrial sharing economy by enabling maintenance and procurement teams at manufacturing leaders like Bosch, Porsche, and Nestlé to reduce MRO spend and optimize inventory through accurate part information, internal transparency, and market visibility.
To achieve this, we have built, and continue to build, an AI\-powered MRO software that connects all players in the spare parts ecosystem, from manufacturers and suppliers to the people working behind the machines. By facilitating the exchange of data, knowledge, and expertise, we create shared visibility that empowers smarter decisions, reduces waste, and drives seamless collaboration. This connected spare parts intelligence unlocks value that extends far beyond software.
Backed by Insight Partners, SPARETECH is accelerating its growth with a strong focus on product innovation and team excellence. We take pride in our inclusive and collaborative culture, as well as our energetic and committed team.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At SPARETECH 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SPARETECH GmbH AI Hiring
SPARETECH GmbH has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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