Interested in this AI/ML Engineer role at Berlin Packaging?
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About This Role
Berlin Packaging is the premier supplier of rigid packaging. With over $3 billion in revenue, we have grown 5x faster than the overall packaging industry since 1898\.
Chances are you enjoy products supplied by us every day! We serve virtually every end market: automotive, beverage, food, home care, industrial chemical, pet care and veterinary, personal health and beauty, wine and spirits, and cannabis.
Our engagement scores are 2x the national average, and every employee shares in Berlin's profitable growth. Combined with our "Anything is Possible" mindset and our winning culture, our 2,000\+ employees agree, we love it here!
Berlin Packaging has been investing in technology across the enterprise, and this role leads the charge on AI and automation. As our AI \& Automation Lead, you will have direct empowerment to identify, design, and build solutions that drive real operational impact — spanning intelligent workflows, process automation, and AI\-powered applications across the enterprise.
This is a hands\-on technical lead role. You will be the primary architect and practitioner, working alongside a small team of onshore and offshore engineers to deliver end\-to\-end applications to our internal and external audiences. You set the technical direction, you stay close to the code, and you're accountable for what ships.
The ideal candidate has done this before. You know how to navigate a complex enterprise environment, earn trust quickly, and deliver working solutions that create real business value — without waiting to be told exactly how.
What You'll Do
Design \& Build AI\-Powered Solutions
- Architect and develop AI and automation solutions across the enterprise — from intelligent workflows and LLM\-powered applications to system integrations and data pipelines
- Evaluate and implement the right tools and platforms for each use case, drawing on a broad command of the AI and automation landscape
- Prototype rapidly, validate with stakeholders, and ship — iterating quickly based on real\-world feedback
- Own the full delivery lifecycle: requirements, development, testing, deployment, and ongoing improvement
- Set the architectural standards and engineering practices that ensure solutions are scalable, maintainable, and production\-grade
Drive Business Impact
- Partner with product leaders to identify and prioritize automation opportunities with clear ROI
- Translate business problems into well\-scoped technical solutions — and deliver them without heavy oversight
- Maintain a forward\-looking roadmap that balances near\-term wins with longer\-term capability building
- Communicate outcomes in business terms, keeping stakeholders informed and confident in the work
Lead the Team
- Provide technical direction and hands\-on guidance to a small team of onshore and offshore engineers
- Set clear expectations, review work, and maintain quality and velocity across the team
- Foster a collaborative, high\-accountability environment where engineers grow and delivery stays on track
- Build the documentation, standards, and practices that keep the team aligned and the codebase healthy
Required Qualifications
- 5\+ years of hands\-on software development experience, with significant depth in AI/ML, intelligent automation, or related disciplines
- A proven practitioner — you have a track record of delivering production AI and automation solutions in complex enterprise environments, not just proofs of concept
- Strong proficiency in Python and/or JavaScript/TypeScript; fluency with REST APIs, microservices, and cloud platforms (AWS, Azure, or GCP)
- Deep hands\-on experience with LLM integration (OpenAI, Anthropic, Azure OpenAI, or similar), including prompt engineering and agentic workflow design
- Working knowledge of RPA platforms (UiPath, Power Automate, or similar) and workflow orchestration tools
- Strong grasp of enterprise system integration patterns, data pipelines, and API\-driven connectivity
- Experience leading or technically directing a small team of engineers, including offshore or distributed team members
- Credible with business stakeholders; able to earn trust quickly and communicate the value of technical work in plain language
Preferred Qualifications
- Experience in manufacturing, distribution, supply chain, or packaging industries
- Hands\-on experience with Power Automate, Azure OpenAI, Salesforce AgentForce, or other enterprise platforms in an automation/AI context
- Familiarity with Agile or lightweight delivery frameworks suited to small, fast\-moving teams
- Exposure to MLOps practices: model deployment, versioning, monitoring, and lifecycle management
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience)
Berlin Packaging offers an outstanding compensation and benefits package including:
- Competitive PTO including vacation, personal days, holidays, and sick time
- 1Berlin Shared Ownership Plan
- Profit sharing
- 401(k) with company match
- Medical, Dental, and Vision insurance
- Health Savings Account (HSA)
- Flexible spending accounts (FSAs) for transportation, medical, and dependent care expenses
- Paid parental leave for up to 12weeks
- Health club reimbursement
- Tuition reimbursement
- 529 college savings plan
- Employee Assistance Program for mental health and well\-being
- Calm app to improve mental wellbeing
- Employee referral program
- Company sponsored short\- and long\-term disability and life insurance with optional voluntary life insurance
At Berlin Packaging, we look at candidates' skills but hire based on traits. We believe in hiring smart, passionate people who are enthusiastic to learn and develop their skills. If you're excited about this role but your experience doesn't align perfectly with the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Berlin Packaging provides equal employment opportunities for all employees and applicants for employment without regard to race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth and related medical conditions), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, protected medical condition as defined by applicable state or local law, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances.
Visit our Careers page for more information on our *Anything is Possible* culture and Total Rewards.
- *Note: any salary information shared is a general guideline only. Pay is based on candidate skills, experience, and qualifications, as well as market and business considerations.*
- *This position is not eligible for sponsorship.*
*\#LI\-SW1*
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Berlin Packaging, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Berlin Packaging AI Hiring
Berlin Packaging has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $130K - $130K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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