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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 the AI Engagement Manager, you are the operational backbone of the engagement — the person who makes complex, multi\-stakeholder AI engagements actually move. This is an orchestration role, not a commercial one; success is measured by getting partners to value, not by signings or P\&L. You own the partner relationship from discovery through workflow adoption, hold the team accountable to delivery, and serve as the primary bridge between field execution and R\&D — all while managing multiple engagements simultaneously in an environment where the playbook is still being written.
About the Job
- Own the full AI engagement arc. From first discovery through sustained workflow adoption — serving as the external partner's single point of contact across every phase while driving scope, timelines, milestones, and escalations and keeping cross\-functional workstreams aligned and moving. There is no "launch and hand off" in this model.
- Drive requirements and delivery precision. Shape engagement structure, align on objectives, translate partner needs into a clear execution plan, and track against it with rigor. Surface risks early and keep stakeholders — partner and internal — ahead of problems, not behind them.
- Champion continuous quality. In AI engagements there's no single gate — value is proven iteratively. You oversee the testing and feedback loops that ensure the platform is performing against real partner workflows, track adoption progress, and stay on top of the change management work that determines whether a deployment becomes a lasting behavior change.
- Hold the R\&D feedback loop. You are accountable for ensuring field learnings flow back to R\&D and that R\&D delivers on agreed roadmap commitments. When either side of the loop breaks down, you own the escalation.
- Build playbooks. Identify reusable patterns across engagements and turn them into scalable delivery frameworks the whole pod can use. We are early; what you build now will become the foundation for how this team operates at scale.
- Own engagement economics. Track the full cost structure of every active engagement — resource allocation across the pod, time spent per workstream, and cost card inputs — and maintain a running view of cost\-to\-deliver across the portfolio. Surface cost/benefit signals to inform prioritization and go/no\-go decisions on new engagements. As the pod scales, your cost data becomes the foundation for delivery efficiency benchmarks.
- This is a field\-first role. Travel is part of the job — expect to be on\-site at partner locations during key engagement phases, with travel that could reach 50%\+ at peak. Presence matters in this model.
About You
Minimum Qualifications
- 8\+ years in technical project management, enterprise engagement management, or a related field — with a track record of delivering complex, multi\-stakeholder technical programs end to end to external enterprise clientele.
- Demonstrated experience managing programs across the full software development lifecycle — from requirements definition and scoping through build, test, and launch — including the ability to identify and manage risk, develop detailed work plans, and allocate resources across concurrent workstreams.
- Technical fluency sufficient to hold credible conversations with partner engineering teams, understand AI/ML implementations and integration dependencies, and manage technically complex delivery workstreams without being the technical expert in the room.
- Experience building and maintaining engagement cost models — tracking resource utilization, time allocation, and cost inputs across concurrent programs and translating those into cost/benefit analysis that informs business and deal\-level decisions.
- Exceptional communication, organizational, and time management skills, paired with a sharp attention to both details and overarching project objectives. An expert in facilitating highly productive internal and external meetings \- and creating assets and processes which drive project clarity at every step.
- Comfort operating in ambiguous, fast\-moving environments where structure doesn't yet exist.
- Proficiency across program management tools (Jira, Asana, Smartsheet), collaboration and documentation platforms (Confluence, Google Workspace), and executive\-level presentation tools (PowerPoint, Google Slides).
- Bachelor's degree in Technology, Engineering, Business, or related field, or equivalent practical experience.
- Willingness to travel \~50% within North America.
Preferred Qualifications
- Experience managing AI, data platform, or systems integration deployments at enterprise scale.
- Background in retail tech, e\-commerce, operations, or CPG.
- Experience working at the intersection of engineering, product, and commercial teams.
- Familiarity with agentic AI platforms, LLM APIs, or RAG\-based systems — enough to be conversant with partners and contextualize where Instacart's platform fits.
- Familiarity with CRM tools (Salesforce), BI and reporting platforms (Tableau, Looker, Power BI), and workshop facilitation tools (Miro, Lucidspark) — enough to stay aligned with pipeline, interpret engagement metrics, and run a working session when needed.
- PMP or equivalent program management credentials.
- 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
$214,000 \- $226,000 USD
WA
$205,000 \- $216,500 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$196,000 \- $207,000 USD
All other states
$178,000 \- $188,000 USD
Salary Context
This $205K-$226K range is above the median 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 ($215K) sits 16% above the category median. Disclosed range: $205K to $226K.
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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