Interested in this AI/ML Engineer role at Gopuff?
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About This Role
Gopuff is the go\-to instant commerce platform, delivering everyday essentials in minutes. Powered by a network of fulfillment centers and cutting\-edge technology, we serve millions of customers across the US and beyond. Data Science sits at the heart of how we delight customers, optimize operations, and grow our advertising business — and we're looking for a leader to push all three forward.
As Director of Data Science, you will lead a high\-impact team responsible for the models and intelligence that power Gopuff's consumer experience, delivery network, and advertising platform. This is a hands\-on leadership role — you'll set technical direction, architect solutions, and write code alongside your team. You'll partner closely with Product, Engineering, and Business leaders to translate complex ML capabilities into measurable business outcomes.
### What We Offer
- Medical/Dental/Vision Insurance
- 401(k) Retirement Savings Plan
- HSA or FSA eligibility
- Long and Short\-Term Disability Insurance
- Fitness Reimbursement Program
- 25% employee discount \& FAM Membership
- Flexible PTO
- Group Life Insurance
- EAP through AllOne Health (formerly Carebridge)
### What You'll Do
Leadership \& Strategy
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- Own the end\-to\-end data science roadmap across Consumer, Delivery, and Ads — translating business priorities into a coherent ML strategy with clear milestones and measurable ROI.
- Provide strong technical direction and mentorship to a team of Data Scientists and ML Engineers; establish best practices for model development, evaluation, and deployment.
- Partner with C\-suite and senior product leadership to influence product strategy and build organizational confidence in ML\-driven decision\-making.
- Drive a culture of experimentation: define measurement frameworks, champion A/B testing rigor, and hold the team accountable to business impact.
Consumer — Search, Recommendations \& Personalization
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- Lead the design and continuous improvement of Gopuff's search ranking and query understanding systems, including semantic search and intent modeling.
- Build and scale personalization infrastructure that adapts the customer experience in real time — from homepage carousels to dynamic upsell and cross\-sell surfaces.
- Develop next\-generation recommendation models (collaborative filtering, two\-tower retrieval, contextual bandits) that drive basket size and repeat purchase.
- Partner with Product to define upsell and nudge strategies grounded in behavioral signals and causal inference.
- Work day to day with gopuff engineering teams to bring search and recommendation changes to life
Delivery Intelligence
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- Own the predictive models powering ETA accuracy, dynamic dispatch, and driver routing that underpin Gopuff's speed promise.
- Apply ML to optimize zone coverage, demand forecasting, and fleet utilization — directly impacting contribution margin.
- Partner with Operations to turn model outputs into actionable tooling for fulfillment center and driver teams.
Ads \& Monetization
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- Architect and own Gopuff's ad ranking stack — query\-ad relevance scoring, multi\-objective ranking (revenue × customer experience), and auction mechanics.
- Build CTR/CVR prediction models and closed\-loop attribution pipelines for sponsored product, display, and offsite formats.
- Define and improve advertiser\-facing ML products: bid optimization, budget pacing, audience targeting, and incrementality measurement.
- Collaborate with the Ads Product and Sales teams to grow advertiser ROI while protecting the organic shopping experience.
### What We're Looking For
Required
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- 8\+ years in applied data science or ML, with at least 3 years managing teams of scientists and engineers in a fast\-paced tech or e\-commerce environment.
- Ad ranking or retrieval systems in e\-commerce, marketplace, or search contexts — including relevance modeling and multi\-objective optimization. Proven experience building and shipping
- two\-tower retrieval, transformers, LLMs, contextual bandits, GNNs, and causal/uplift modeling. Deep expertise in modern ML architectures:
- Strong product intuition and business acumen — you can connect model improvements to revenue, NPS, and operational metrics and communicate this clearly to executives.
- proficient in Python and comfortable diving into model code, experiment pipelines, and production systems. Hands\-on coder:
- familiar with feature stores, model registries, real\-time serving, and experimentation platforms. Experience with large\-scale ML infrastructure:
- Track record of building and retaining diverse, high\-performing data science teams.
Preferred
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- Prior leadership at a consumer marketplace, quick\-commerce, grocery, or retail media company.
- Familiarity with retail media network (RMN) measurement standards and privacy\-preserving attribution techniques.
- Experience with real\-time personalization at scale, including streaming feature pipelines. Familiarity with Databricks and Snowflake is a plus.
- Publications or presentations at NeurIPS, KDD, RecSys, SIGIR, or equivalent.
### Why Gopuff?
- Unique data moat: real\-time demand signals from millions of orders, deep catalog and fulfillment data, and a first\-party advertising signal set most companies can only dream about.
- High\-autonomy, high\-impact: you'll report to senior leadership and own the roadmap — no bureaucracy between your ideas and production.
- Greenfield opportunity: many of these ML capabilities are being built from the ground up, giving you the chance to architect lasting systems.
- Competitive compensation package including equity, performance bonus, and comprehensive benefits.
### Compensation
- Gopuff pays employees based on market pricing and pay may vary depending on your location. The salary range below reflects what we’d reasonably expect to pay candidates. A candidate’s starting pay will be determined based on job\-related skills, experience, qualifications, interview performance, and market conditions. These ranges may be modified in the future. Exceptions may be made for exceptional individuals. For additional information on this role’s compensation package, please reach out to the designated recruiter for this role.
- This role is eligible for a discretionary annual cash bonus and participation in Gopuff’s equity incentive plan.
- Base Salary Range: $215,000 \- $275,000
At Gopuff, we know that life can be unpredictable. Sometimes you forget the milk at the store, run out of pet food for Fido, or just really need ice cream at 11 pm. We get it—stuff happens. But that’s where we come in, delivering all your wants and needs in just minutes.
And now, we’re assembling a team of motivated people to help us drive forward that vision to bring a new age of convenience and predictability to an unpredictable world.
Like what you’re hearing? Then join us on Team Blue.
\#LI\-GOPUFF
*Gopuff is an equal employment opportunity employer, committed to an inclusive workplace where we do not discriminate on the basis of race, sex, gender, national origin, religion, sexual orientation, gender identity, marital or familial status, age, ancestry, disability, genetic information, or any other characteristic protected by applicable laws. We believe in diversity and encourage any qualified individual to apply.*We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $215K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gopuff, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($245K) sits 12% above the category median. Disclosed range: $215K to $275K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Gopuff AI Hiring
Gopuff has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $275K - $275K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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