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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
The Enterprise AI Pod is a forward\-deployed, field\-first unit within Instacart's Enterprise Solutions team — purpose\-built to take the Instacart Intelligence Platform to market with B2B retail and CPG partners. As an AI Field Consultant, you bring the deep vertical expertise in grocery retail and CPG that makes the pod credible in the field — earning trust at every level of a partner organization, from analyst to executive, and translating what you hear into AI use cases R\&D can build and partners will actually adopt. This is a small, senior, cross\-functional pod operating at the intersection of discovery, co\-creation, and change management — early, defining the playbook as we go, and looking for people who are energized by that.
About the Job
- Be the domain credibility of the pod. You know grocery retail and CPG — merchandising, supply chain, store ops, trade, e\-commerce — well enough to walk into any room at any level and earn it immediately. As the pod scales, you'll deepen into a vertical and become Instacart's sharpest expert in that space.
- Get to the real problem. Embed with partner teams to identify where agentic AI will actually move the business — not just what's technically possible, but what will change how operators work day to day. Translate those problems into AI\-solvable use cases the pod can build against.
- Lead discovery and requirements. Run structured workshops at partner sites, surface and prioritize the use case map, and turn field learnings into clear inputs for the pod and R\&D. You're the bridge between what partners need and what gets built.
- Show, don't just tell. Lead hands\-on demos and platform walkthroughs tailored to the partner's specific workflows — making abstract AI capability concrete and compelling in the room. The ability to demonstrate the art of the possible is as important as the ability to articulate it.
- Own adoption. Drive the change management that makes deployments stick — new operating cadences, workflow redesigns, user training. Success is measured by whether team behavior changes, not whether software ships.
- This is a field\-first role. Travel is a core part of the job — expect to be on\-site at partner locations regularly, potentially 50%\+ of the time. If you do your best work embedded with a team rather than behind a desk, you'll thrive here.
About You
Minimum Qualifications
- 10\+ years of layered experience — vertical industry expertise inside grocery retail or CPG, B2B consulting experience translating domain knowledge into business solutions, and ideally time at a technology or AI company where you've seen how platforms are built and sold.
- Deep domain fluency across at least one grocery vertical — merchandising, supply chain, store ops, ecommerce, trade marketing, or CPG — sufficient to earn the trust of a category manager, VP of supply chain, or CPG trade lead from the first conversation.
- Demonstrated ability to run consultative discovery with senior enterprise stakeholders and translate findings into clear, structured requirements.
- Experience leading organizational change management — training, workflow redesign, and embedding new operating cadences with business teams.
- Exceptional communication and executive presence — comfortable presenting to C\-suite and operational audiences alike, with comfort operating in ambiguous, greenfield environments.
- Proficiency in BI and analytics tools (Tableau, Looker, Power BI) and workshop facilitation tools (Miro, Lucidspark) — you need to know what you're displacing or augmenting and how to map what replaces it.
- Bachelor's degree in Business, Engineering, Operations, or related field, or equivalent practical experience.
- Willingness to travel \~50% within North America.
Preferred Qualifications
- Familiarity with AI and ML concepts — including LLMs and agentic systems — sufficient to connect platform capabilities with partner workflow needs. Partners will reference these, and you need to be conversant enough to contextualize where Instacart's platform fits.
- Exposure to agentic AI systems, RAG pipelines, or LLM\-based product development.
- Experience working within systems partners actually run: Snowflake (dominant in grocery/CPG analytics), ERP platforms (SAP, Oracle), TPM/TPO tools, and POS systems.
- Track record contributing to product and engineering roadmap conversations via structured voice\-of\-customer feedback.
- Strength using data and storytelling to influence decisions and secure investment in operational improvements.
- Prior work in a forward\-deployed, embedded, or startup environment.
\#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
$242,000 \- $255,500 USD
WA
$232,000 \- $245,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$222,000 \- $234,500 USD
All other states
$202,000 \- $213,000 USD
Salary Context
This $222K-$255K 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($238K) sits 29% above the category median. Disclosed range: $222K to $255K.
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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