Staff Machine Learning Engineer

Below is a quick‑reference summary of the Staff Machine‑Learning Engineer – Sponsored Ads role at Chewy, followed by a few suggestions on how you can position yourself (or a candidate) for this opportunity.

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1️⃣ Role at a Glance

Category	Details
Team / Org	Sponsored Ads Technology, Bellevue, WA (remote/hybrid possible)
Level	Staff (individual contributor, senior technical leader)
Reports to	Senior Engineering / Product leadership on the Sponsored Advertising team
Core Mission	Build and ship ML/DL systems that drive product discovery, relevance, ranking, click‑through prediction, dynamic bidding, and auction mechanics for Chewy’s on‑site and off‑site ad inventory.
Key Impact	• Increase ad revenue while improving shopper experience (pet‑parents). • Enable brand owners to reach the right audience. • Shape technical strategy across search, relevance, and auction pipelines.
Primary Responsibilities	1. End‑to‑end model lifecycle – ideation → experimentation → production at Chewy scale. 2. Collaborate with product & engineering to translate business goals into ML solutions (ranking, relevance, CTR, bidding, keyword recommendation, campaign optimization). 3. Research & publish – produce papers for top ML/AI/Advertising conferences. 4. Mentorship – set performance bar, define best practices, coach junior scientists/engineers. 5. Stakeholder communication – present findings & road‑maps to senior leadership and non‑technical business partners.
Must‑Have Skills / Experience	• Advanced degree (M.S./Ph.D.) in Operations Research, Statistics, Applied Math, Data Science or equivalent industry experience. • 8+ years of professional ML work (modeling, pipelines, production). • Deep knowledge of advertising systems (selection, ranking, auction, bidding) – a strong plus. • Proven ability to design & ship large‑scale distributed pipelines (Spark, Flink, Beam, Ray, etc.). • Hands‑on experience with predictive models (time‑series, regression), linear programming / optimization, classification, search & ranking, and large‑scale embeddings. • Strong mathematical foundation and ability to explain complex concepts in business terms.
Nice‑to‑Have / Bonus	• Direct experience in e‑commerce/retail advertising. • Prior work on ad‑tech platforms (DSP, SSP, search ads, recommendation). • Familiarity with AWS ML services (SageMaker, Personalize, Glue, EMR) or comparable cloud ML stacks.
Compensation & Benefits	• Base salary $186.5 k – $297.5 k (range varies by location/experience). • Annual equity grant, potential bonus (C08 level). • 401(k) + matching, unlimited PTO, 6 paid holidays, comprehensive health/vision/dental, life & disability, parental leave, pet‑adoption reimbursement, employee assistance, discounts (10 % pet‑insurance, 20 % Chewy.com).
Culture & Inclusion	Chewy emphasizes diversity, equity, inclusion, and provides accommodations under ADA and similar laws.

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2️⃣ How to Position Yourself (or a Candidate)

Area	What to Highlight	Example Talking Points / Resume Bullets
Domain Expertise	Direct experience with ad‑tech (ranking, auction, bidding) or e‑commerce advertising.	“Designed a real‑time bidding engine for a DSP that reduced latency by 35 % and increased win‑rate by 12 %.”
End‑to‑End ML Delivery	From research → prototype → A/B test → production at scale (millions of requests/queries).	“Led the full lifecycle of a CTR prediction model serving >200 M daily impressions, achieving a 9 % lift in click‑through rate.”
Scalable Infrastructure	Distributed data pipelines, feature stores, model serving (Kubernetes, Flink, Spark, Ray, TF‑Serving, etc.).	“Built a feature‑store on AWS Glue + DynamoDB that served >5 TB of daily features with sub‑second latency.”
Optimization & Operations Research	Linear programming, mixed‑integer programming, reinforcement learning for bid optimization.	“Formulated and solved a mixed‑integer program to allocate daily ad budget across 10 k campaigns, improving ROI by 15 %.”
Research & Publication	Papers, patents, conference talks (KDD, SIGIR, WWW, RecSys, ICML, NeurIPS).	“Co‑authored a KDD paper on hierarchical embeddings for product recommendation; citation count 27.”
Leadership & Mentorship	Managing or mentoring junior ML engineers/scientists, establishing best‑practice guidelines.	“Mentored a team of 4 junior data scientists; instituted code‑review and model‑validation standards that reduced production bugs by 40 %.”
Communication Skills	Translating technical results to product, marketing, and executive audiences.	“Presented quarterly ad‑performance insights to senior leadership, influencing roadmap prioritization for three new ad formats.”
Cloud & ML Ops	Experience with AWS SageMaker, Personalize, or similar managed services; CI/CD for ML.	“Implemented CI/CD pipelines using SageMaker Pipelines and GitHub Actions, cutting model‑deployment time from weeks to hours.”
Cultural Fit	Passion for pets/e‑commerce, commitment to inclusive teamwork.	“Volunteer pet‑adoption advocate; championed internal DEI hackathon focused on accessibility of ad tools.”

Resume Tip: Use a “Key Impact” bullet format (Problem → Action → Result) and quantify results (e.g., “+12 % CTR”, “served 200 M daily requests”, “reduced latency from 120 ms to 45 ms”).

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3️⃣ Sample Interview Preparation Checklist

Topic	Sample Questions / Tasks
Ads Fundamentals	Explain the difference between CPC, CPM, CPA. How does a second‑price auction work? What are the main challenges in real‑time bidding?
Ranking & Relevance	Design a learning‑to‑rank pipeline for sponsored product search. Which loss functions would you consider?
CTR / Conversion Modeling	Walk through feature engineering for a high‑cardinality categorical field (e.g., product ID). How would you handle cold‑start items?
Optimization	Formulate a linear program to allocate a daily ad budget across multiple campaigns with ROI constraints.
Scalable Systems	How would you build a feature store that supports both batch and online feature retrieval at >10 k QPS?
Model Deployment	Compare SageMaker Pipelines vs. custom Kubernetes + KFServing for continuous training.
A/B Testing & Evaluation	Describe a robust experiment design to evaluate a new bidding algorithm. What metrics would you monitor?
Research & Publication	Discuss a recent paper (e.g., “DeepFM”, “Transformer‑based ranking”) and how you could adapt it to Chewy’s ad stack.
Leadership	Tell a story where you mentored a junior teammate through a production incident.
Behavioral / Culture	Why are you excited about working on pet‑parent shopping experiences? How do you champion diversity in a technical team?

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4️⃣ Quick Action Items

1. Tailor your résumé – add the impact‑focused bullets above, especially any ad‑tech or e‑commerce experience.
2. Prepare a 2‑minute “elevator pitch” – why you’re the ideal fit for Chewy’s Sponsored Ads team (focus on ML expertise, scale, and passion for pets).
3. Gather artifacts – links to published papers, open‑source contributions, or internal project write‑ups you can discuss.
4. Research Chewy – familiarize yourself with Chewy’s product catalog, recent ad‑related blog posts, and any public patents or conference talks from their team.
5. Mock interview – practice explaining a complex ML pipeline to a non‑technical stakeholder (e.g., “How does our CTR model improve a pet‑parent’s search experience?”).

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TL;DR

Chewy is looking for a senior ML leader who can design, ship, and scale sophisticated advertising models (ranking, CTR prediction, dynamic bidding, auction) while driving research and mentoring a growing team. Success hinges on deep ad‑tech knowledge, large‑scale ML engineering, strong quantitative skills, and the ability to translate technical breakthroughs into business value for shoppers, brand owners, and Chewy alike. If your background aligns with the “must‑have” list and you can demonstrate measurable impact at scale, you’re a strong candidate for this Staff Machine‑Learning Engineer role. Good luck!