Interested in this AI/ML Engineer role at Flex?
Apply Now →Skills & Technologies
About This Role
Flex is looking for a VP of Retail Sales to lead and scale our retail business to $50mm+ over the next 3 to 4 years.
This role owns national retail revenue across mass, drug, and grocery. You will expand distribution, unlock new channels, deepen existing partnerships, and build a sales organization that can scale with the business.
You will personally drive high-impact revenue opportunities while coaching and developing a team of experienced sales managers. You will report directly to the cofounder and operate as a core member of the leadership team.
Base Salary Range: $225-$245k per year, commensurate with experience and equity stock options
Responsibilities
What You’ll Be Accountable For:
- Scaling Flex’s retail revenue to $50mm+ with profitable, sustainable growth
- Expanding distribution and velocity within mass, drug, and grocery
- Opening new channels where Flex is under-penetrated (e.g., Costco, convenience, additional grocery banners)
- Owning national retailer strategy, negotiations, line reviews, and joint business planning
- Leading and developing a team both internal (sales leaders) and external (brokers)
- Partnering closely with CEO, Marketing, Finance, Supply Chain, and Product to align growth, margin, and execution
- Building a repeatable retail growth engine, not just hitting this year’s number
What You’ll Do:
- Personally lead high-stakes retail pitches, negotiations, and expansion initiatives
- Set the retail growth strategy and translate it into executable plans by channel and customer
- Coach and level up the existing sales team; hire selectively as the business scales
- Own retailer economics: pricing, trade spend, margins, promotional strategy
- Hold a high bar for accountability, forecasting discipline, and execution without turning the role into ops
- Be the voice of the retailer internally, and the voice of Flex externally
Requirements
Who You Are:
- You have scaled a retail/wholesale CPG business above $50mm and know what actually breaks at that stage
- You are a hunter by instinct, not just by resume
- You are equally comfortable closing a major account and coaching a sales manager through a tough line review
- You understand mass, drug, grocery retail dynamics deeply and are also familiar in other categories (e.g., dollar, big box, convenience)
- You can articulate a compelling reason why women’s health / femcare is the right category for you to lead
- You move fast, take ownership, and don’t hide behind process
- You want real responsibility, not a “nice” VP title
Necessary Experience:
- Senior Director or VP-level experience leading retail sales in CPG
- Direct experience selling into mass, drug, and/or grocery
- Proven success managing the world's largest retailers/accounts
- Experience managing and developing sales leaders
- Comfort operating in a founder-led, high-expectation environment
Why This Role Is Hard (and Worth It):
- Mission driven making a huge difference in peoples’ lives (look at our reviews)
- The business already works - your job is to make it much bigger
- Growth will come from both new doors and doing more with existing ones
- You’ll be expected to lead, not wait for perfect information
- You’ll have real influence on the future shape of the company
Our shared values
- Lead with Intention: Leaders are made not born. Leadership is a practice of intention. And through that intention is how we will ultimately succeed in realizing our mission and vision.
- Embrace Accountability: We are imperfect in our actions, results, and even sometimes our intentions. By making accountability a practice, we destigmatize failure, increase trust with others, and accelerate learning both at the individual and the team level.
- Practice a Growth Mindset: Growth comes at the edge of our comfort zone. We repeatedly place ourselves there by risking failure and embracing the challenges that failure presents us to own our growth and support others in theirs.
- Be You: We want people to show up as they are because that creates a healthier, more dynamic, and effective work environment. Just as much to do with being oneself, it’s everyone’s responsibility to create space for others to be their authentic self as well.
About Flex
The Flex Company was founded on the belief that people deserve innovative, sustainable, life-changing period products. After years of disappointment and discomfort trying dozens of products, Lauren Schulte Wang founded Flex to create body-safe, medical grade alternatives that outperform traditional period products. Flex Disc and Flex Cup generate 60% less waste and have the capacity of up to three super tampons, all while maintaining the highest level of comfort through inventive engineering and rigorous testing.
Flex is sold at over 28,000 stores across the US and is the #1 better for you period brand based on units sold. Committed to making its life-changing products accessible to as many people possible, Flex is available at Target, CVS, and Walgreens, and at flexfits.com.
Flex is an equal opportunity employer. We recruit, employ, train, compensate, and promote regardless of race, ethnicity, religion, sex, gender, age, and other protected categories. From our hiring practices to the design of our flagship products, we believe equity and diversity is critical to the ideas, talent, and processes that help us create the most positive impact for our customers and for each other. We encourage people of all backgrounds and identities to apply to be a team member here. Moreover, Flex considers for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.
Flex collects personal information from candidates as part of the application and hiring process. California residents can view our CCPA Notice at Collection and Privacy Policy, which serves as our "Notice of Collection" for applicants and employees under the CPRA.
We pay competitive salaries, equity, & benefits including (but not limited to) medical, dental and vision health insurance, 401k, paid parental leave, open PTO, 401k, and a $1,000 annual learning credit.
hOjega2Hij
Salary Context
This $225K-$245K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Flex, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($235K) sits 53% above the category median. Disclosed range: $225K to $245K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Flex AI Hiring
Flex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $245K - $245K.
Location Context
AI roles in Los Angeles pay a median of $179,440 across 1,356 tracked positions. That's 6% 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.