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About This Role
Retail Product Manager
In addition to our core direct\-to\-consumer business, Factor\_ has more recently expanded into the retail channel, making our meals available for purchase at brick\-and\-mortar stores. We are seeking a strategic, data\-driven, and highly collaborative Retail Product Manager to own Factor\_'s Retail Prepared Meals and adjacent food and beverage categories to support growth in this channel. In this pivotal role, you will champion the end\-to\-end retail product lifecycle—from driving overarching category strategy and formulation management to leading packaging design and continuous portfolio optimization. Acting as the ultimate product expert and a crucial cross\-functional linchpin, you will partner seamlessly with Sales, Operations, DTC Product, and Nutrition teams to bridge deep consumer insights with specific retailer needs. Your core objective is to engineer a winning, scalable retail portfolio that commands attention on the shelf, drives exceptional sales velocity for our partners, and builds enduring brand loyalty through relentless, customer\-centric innovation.
Key Responsibilities:
- Retail Product Strategy \- Develop a winning retail prepared meals range consisting of core year\-round SKUs, seasonal skus and may include the development of exclusive retailer ranges. Identify opportunities for expansion into adjacent categories based on retailer needs, white space and consumer research, as well as products available in our portfolio or ripe for development.
- Portfolio Lifecycle Management \- Evaluate existing meals, conduct sales performance reviews, leverage online ratings and commentary as well as market trends to optimize our product portfolio by retailer.
- Formulation \& Spec Management \- Ensure the retail product range can consistently be offered to retail partners by planning for necessary formulation, ingredient, and packaging changes, owning the spec creation and management process.
- Packaging Design \- Own packaging design for retail products, ensuring alignment with the broader F\_ brand and packaging creative guidelines, meeting retailer compliance standards and functionality and appeal on shelf.
- Sales Support \- Support sales and review meetings with retailers as the product expert, sharing performance insights, industry trends, and product strategy and development opportunities.
- Cross\-Functional Management \- Partner with key members of the product team to ensure a strong understanding of the DTC product strategy, dietary lifestyles, consumer insights, and product development opportunities with the new formats team. Additionally, partner with operations, nutrition, FSQA, sourcing, retail team members, etc. to ensure the successful management of our retail program.
You are…
- A problem solver \- You are resourceful and scrappy and aren't afraid to roll up your sleeves and figure it out. Where a process doesn't exist, you'll create one.
- Curious \& Passionate about Product \- you are always looking to learn more about the industry, new products and ways we can delight our customers.
- Data\-driven to the core and leverage data to make key business decisions.
- Detail\-oriented – You possess strong organizational skills and demonstrate a methodical approach to your work, documenting new processes.
- A project manager \- You have excellent project management skills leading groups, as well as a demonstrated ability to meet deadlines and complete deliverables on time.
- A critical thinker – You use logic to identify opportunities, evaluate alternatives, and present critical information to solve complex problems.
- Highly collaborative \- You enjoy working collaboratively with cross\-functional teams to get things done.
At a minimum, you have...
- Bachelor degree in Business Administration or other relevant field of study.
- Minimum of 5 years of relevant experience with a background in physical product management, category management, product marketing or strategic sourcing across food/food tech or CPG industries.
- Strong preference for experience within CPG or retail product development.
- Fluent in Google Suite applications (especially sheets \& slides), tableau, Gemini and an ability to quickly learn and leverage a range of other databases and tech tools.
- Familiarity with retail data platforms (NIQ, SPINS, or Circana/IRI) to track category performance and support buyer conversations.
- Ability to successfully manage multiple cross\-functional stakeholders as well as to present to executive level colleagues.
- Success in collaborating with cross\-functional teams to get things done
- Travel: \<10%
You'll get…
- Competitive salary, 401k with company match that vest immediately upon participation, and company equity plan based on role
- Generous PTO, including sabbatical, and parental leave of up to 16 weeks
- Comprehensive health and wellness benefits with options at $0 monthly, effective first day of employment
- Tuition reimbursement for continuing education (upon 2 years of service)
- Up to 85% discount on subscriptions to HelloFresh meal plans (HelloFresh, Green Chef, Everyplate, and Factor\_)
- Access to 7 different Employee Resource Groups (ERGs) including those for BIPOC, women, veterans, parents, and LGBTQ\+
- Inclusive, collaborative, and dynamic work environment within a fast\-paced, mission\-driven company that is disrupting the traditional food supply chai
This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.
*Our company is committed to fair hiring practices and complies with all applicable laws, including the Colorado Job Application Fairness Act (JAFA). In accordance with JAFA, we will not request your age, date of birth, or dates of attendance at or graduation from an educational institution on your initial application for employment.*
*When submitting supporting documents such as a resume, curriculum vitae (CV), or educational transcripts, you may voluntarily redact or omit any information that would identify your age. This includes:*
- *Dates of birth*
- *Dates of attendance at educational institutions*
- *Dates of graduation*
*Your decision to redact this information will not adversely affect the consideration of your application. We evaluate all candidates based on their skills, qualifications, and experience. Please be aware that should you receive a conditional offer of employment, we may be required to request this information for legally permissible purposes, such as verifying eligibility for employment or for benefits administration and background checks.*
*\#Factor \#JD1008*
Salary Context
This $121K-$137K range is above the median 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 HelloFresh, 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 ($129K) sits 22% below the category median. Disclosed range: $121K to $137K.
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.
HelloFresh AI Hiring
HelloFresh has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $88K - $188K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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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