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About This Role
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one\-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in\-person events. Learn more about our flexible approach to where we work.
Overview
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Instacart’s Agentic Commerce team builds conversational and generative AI products that deliver hyper personalized shopping experiences at massive scale. Our portfolio includes Cart Assistant—Instacart’s AI shopping agent helping people plan what to eat and shop for groceries—as well as integrations with leading consumer agents such as Claude, ChatGPT, and Gemini. These products serve millions of consumers and hundreds of retail partners, helping retailers differentiate and win in the AI era.
As the Director of Product for Agentic Commerce, you will lead a team of product managers and partner closely with Engineering, ML, Design \& Research, GTM, and Business Development to set the multi\-year vision for agentic commerce at Instacart. You will define and deliver novel, safe, and delightful consumer\-facing AI experiences, drive measurable outcomes for retailers, and shape how Instacart redefines grocery commerce with AI. You’ll thrive here if you’re energized by 0\-to\-1 product building, complexity at scale, and a highly collaborative culture where we roll up our sleeves, communicate effectively, and solve creatively together.
About the Job
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You will own the end\-to\-end product strategy and execution for our agentic commerce portfolio, leading a small but mighty PM team and a cross\-functional group of approximately 25 builders across the company.
- Own the multi\-year product vision, strategy, and roadmap for Agentic Commerce across Cart Assistant and third\-party agent integrations; define clear success metrics and deliver measurable impact on conversion, AOV, retention, and customer satisfaction.
- Lead, develop, and grow a high\-performing team of product managers; establish product operating rhythms, hiring plans, and career development frameworks that cultivate a healthy, high\-trust team.
- Partner with Engineering, ML, and Design to ship high\-quality AI features end to end, including experimentation frameworks, safety/guardrails, model selection and evaluation, latency/reliability, and privacy\-by\-design.
- Collaborate with Retail, Partnerships/BD, and GTM teams to commercialize agentic solutions, drive adoption with retailers, and shape co\-development opportunities with industry\-leading partners.
- Drive cross\-organizational alignment with senior leaders; translate complex, technical concepts into clear narratives; make principled tradeoffs to prioritize the highest\-leverage work in a fast\-changing environment.
- Champion responsible AI practices, ensuring experiences are transparent, trustworthy, and inclusive while meeting regulatory and brand standards.
About You
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### Minimum Qualifications
- 10\+ years of product management experience, including 3\+ years managing and developing product managers.
- Proven track record building successful consumer products at scale with deep expertise in e\-commerce.
- Strong technical fluency with ML\- and AI\-driven products, including collaborating closely with engineering and ML partners on model\- and platform\-level decisions.
- Demonstrated success leading multi\-quarter 0\-to\-1 initiatives and launching net\-new products in complex enterprise, B2B2C, and/or marketplace environments.
- Excellent written and verbal communication skills; adept at simplifying complex topics for senior leaders and driving alignment across organizations.
- Evidence of hiring, developing, and retaining high\-performing PMs; building inclusive, high\-trust teams and product cultures.
- Experience defining and moving business outcomes using clear metrics; comfortable with experimentation and data analysis (partnering with Analytics/using SQL or comparable tools).
- Bachelor’s degree in a relevant field (e.g., Computer Science, Engineering, Business, Data Science) or equivalent practical experience.
### Preferred Qualifications
- Hands\-on experience building generative AI and/or conversational commerce products.
- Background in food or grocery technology and familiarity with retailer e\-commerce ecosystems.
- Marketplace experience, particularly balancing the needs of consumers, shoppers, retailers, and brands.
- Experience building for both enterprise retail customers and end consumers (B2B2C), including co\-developing and commercializing solutions with partners.
- Knowledge of LLM evaluation, prompt design, safety/guardrails, and responsible AI practices.
\#LI\-Remote
Instacart provides highly market\-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
CA, NY, CT, NJ
$291,000 \- $307,000 USD
WA
$279,000 \- $294,500 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$266,000 \- $281,000 USD
All other states
$242,000 \- $255,500 USD
Salary Context
This $291K-$307K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Instacart, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($299K) sits 62% above the category median. Disclosed range: $291K to $307K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Instacart AI Hiring
Instacart has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, Research Scientist. Positions span Remote, US, San Francisco, CA, US. Compensation range: $204K - $330K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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