Interested in this AI/ML Engineer role at Levain Bakery?
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About This Role
Competitive triathletes and passionate bakers, Pam Weekes and Connie McDonald left their careers to open a small bread shop in New York City in 1995 — Levain Bakery.
One day, they baked a BIG chocolate chip walnut cookie as energy for triathlon training. At Levain, a batch of these 6\-ounce cookies flew off the shelves, and an icon was born. The cozy shop on West 74th Street became a neighborhood favorite and a destination for epicurious visitors from around the world, hungry for “the cookie.”
From the start, Pam, Connie, and the team baked everything fresh on\-site each day, donating the day’s leftovers to charity — and we still do, nearly 30 years later. Today, Levain has bakeries across the country (with more to come!), plus ecommerce gift boxes to treat cookie lovers in every state.
Our Vision: To give rise to lifelong memories
Our Mission: To bring together people who treat their work and the craft of baking and all who come to visit with great care.
Every day, we strive to embody our company’s five core values:
- Welcome All\- *We are all in it together*
- Work Hard and Be Kind\- *We pull our weight, we do our best, and take care of each other*
- Lead with Heart\- *We operate with empathy and integrity*
- Rise Up\- *We take initiative to make things better*
- Savor It\- *We are present and find the fun!*
THE ROLE: CERTIFIED TRAINER
Certified Trainers are the gold standard of all things Levain: how we treat each other, our customers, and our products. Certified Trainers are responsible forall aspects of training and coaching within the bakery. Certified Trainers serve as ambassadors of the Levain brand, ensuring that all team members are well\-trained and embody the values and standards of our bakery. Certified Trainers utilize our training system, MIX, to deliver comprehensive training programs and support continuous development.
What We’re Looking For
- People\-forward attitude with exceptional customer service skills; Be proactive and empathetic in assisting customers and team members
- Strong communication \& interpersonal skills; team\-oriented with a positive attitude
- Maintain a high level of professionalism and care: be adaptable, flexible, and patient
- Must be reliable, responsible, and punctual
- Ability to remain calm under pressure in a fast\-paced environment when troubleshooting issues: be consistent, patient, and even keeled
- Detail\-oriented and proactive in identifying and addressing training needs
- Embody and promote the values and standards of Levain Bakery
Job Responsibilities:
Team Collaboration:
- Lead with curiosity and compassion by showing care for your co\-workers and build meaningful connections that broaden your perspective
- Display a strong sense of teamwork: Work collaboratively with team members to ensure smooth store operations
- Facilitate open, clear, and timely communication with your team members and management
- Stay informed about new products, procedures, and promotions
Training and Development:
- Stay informed about new products, procedures, and promotion
- Provide tactful feedback to ensure employees meet company standards
- Facilitate initial and ongoing training sessions for team members and new hires, utilizing company training materials
- Foster continuous learning by encouraging team members to engage in self\-driven development beyond formal training
Customer Service
- Greet customers with a warm and welcoming demeanor
- Ensure every guest receives consistent, accurate, high\-quality service that reflects Levain’s brand standard
- Foster strong, lasting relationships with customers to enhance their sense of belonging and connection to our community surrounding the bakery
- Resolve customer issues promptly and courteously and operate with empathy
Operations
- Consistently adhere to established protocols and maintain the highest standards of excellence to ensure all baked goods meet Levain Bakery’s quality standards
- Adept knowledge of Toast POS system
- Ensure all in\-store and third\-party delivery (3PD) orders are accurate and safely prepared
- Maintain cleanliness in all areas of the store, including customer areas, restrooms, and workspaces
- Assist in restocking and organizing inventory and adhere to guidelines given from Shift Lead or management
Requirements
- Minimum 1 year of food service, QSR, or retail experience; experience in training and coaching preferred
- Proficient in using training systems and technology, such as MIX
- Open availability: ability to work mornings, nights, weekends, and holidays
- Ability to lift/push 25\-50 pounds
- Stand/walk for an 8\-hour shift (excluding a ½ hour break); some locations require walking up and down stairs
- Read and communicate in English, both verbally and in writing
- Utilize basic computer skills (Email, Microsoft Office)
- Must have valid DOH Certification \& additional regional required food handlers’ certifications
Compensation \& Benefits
- $19\-21\.25/hour \+ tips \+ up to 10% quarterly bonus potential
- Health, Vision, Dental Insurance
- Employer\-funded Healthcare Reimbursement Account
- Additional supplemental benefits: Commuter Benefits, Employee Assistance Program, Bike\-Share Reimbursement
- 14 Weeks Fully Paid Parental Leave
- One Week of Paid Time Off
- 401K with 3\.5% Company Match
*We are committed to fostering a diverse, inclusive, and equitable workplace where all employees feel valued and empowered to contribute their unique perspectives.*
Salary Context
This $39K-$43K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Levain Bakery, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($41K) sits 75% below the category median. Disclosed range: $39K to $43K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Levain Bakery AI Hiring
Levain Bakery has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $43K - $43K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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