Asks for

The ad says“Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.”
The ad says“Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.”
The ad says“Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.”
The ad says“10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.”
The ad says“Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.”
The ad says“Demonstrated ability to manage high-cost compute resources, with experience optimizing GPU utilization and cloud spend in a hyper-growth environment.”
The ad says“Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.”
The ad says“Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.”
The ad says“10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.”
The ad says“10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.”
The ad says“Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.”
The ad says“Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.”
The ad says“Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.”
The ad says“Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.”
The ad says“Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).”
The ad says“Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.”

ABOUT QUINCE

Quince is a destination for builders, creators, innovators, and operators who want to come together and challenge the status quo. Our mission is simple: make really high quality essentials for really low prices, fairly and sustainably. We deliver on that mission through a unique manufacturer-to-consumer (M2C) model eliminating the layers of traditional retail that add cost and result in consumers paying more than they need to. We find, build relationships with, and work directly with the manufacturing partners behind some of the world’s finest products. From there, our teams design smart, efficient operational processes and build and deploy proprietary technology, AI, and analytics to help us scale fast.

What began with a small assortment of elevated basics has quickly grown into a cross-category brand spanning apparel, accessories, home goods, and more. Today, tens of millions of people across a growing number of countries come – and return – to Quince because they trust us to deliver.

OUR CULTURE

Quince is a culture built for builders by builders. Our way of working starts with a blank sheet of paper. We question conventional thinking, use technology and data to uncover new opportunities, and move quickly to turn ideas into reality. We aren’t interested in replicating how others do retail. We’re building a better way – at a speed and scale unlike anything that’s been done before.

We dream big and chase the hard problems others shy away from. Rejecting long-held assumptions is part of our company's DNA. Where conventional wisdom says you have to choose – soft or durable, speed or rigor, quality or price – we ask why that trade-off has to exist in the first place.
Our pace is fast and the bar is high because our customers expect a lot from us and we refuse to let them down. We believe the best results come from challenging ourselves, learning from one another, and building on each other's strengths.
Here, responsibility is not determined by role, tenure, or seniority. Every team member - no matter their level - has the opportunity to drive our business and shape our trajectory.

If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.

THE ROLE

Senior Engineering Manager, MLOps

We are seeking a Senior Engineering Manager, MLOps to join our growing team. The ideal candidate is a technical visionary with a proven track record of building and scaling the underlying infrastructure that powers production-grade Machine Learning. You have a deep understanding of the ML lifecycle—from model development and distributed training to automated deployment and real-time monitoring—and you are passionate about treating infrastructure as a product for your "customers": Quince’s Data Scientists and AI Researchers. You are a self-starter who excels at identifying architectural bottlenecks and transforming them into seamless, automated "paved roads" that increase team velocity without sacrificing stability.

Thriving in an environment of rapid growth and ambiguity, you make high-judgment decisions on "build vs. buy" and prioritize technical roadmaps that align directly with e-commerce business outcomes. Above all, you are energized by a culture of distributed decision-making and extreme candor, where you will lead a high-performing team to set new standards for how AI is industrialized at scale to serve Quince customers.

Responsibilities

Define the MLOps Vision & Strategy: Architect a long-term roadmap that transitions ML workflows from manual scripts to a fully automated, self-service platform for all Quince Data Scientists and AI Researchers.
Own the "Paved Road" for Production: Build and maintain the end-to-end infrastructure for model training, deployment, and serving, ensuring researchers can move from "idea to production" with zero friction.
Drive Strategic Prioritization: Partner with business leaders to align infrastructure investments with core e-commerce drivers like real-time personalization, dynamic pricing, and inventory forecasting.
Lead "Build vs. Buy" Evaluations: Make high-judgment decisions on when to leverage cloud-native services (e.g., SageMaker, Vertex AI) versus building custom internal tools to optimize for cost, speed, and flexibility.
Guarantee System Scalability & Reliability: Oversee the uptime and performance of production ML services, ensuring the stack can handle massive traffic surges and seasonal spikes without degradation.
Manage Compute Governance & Costs: Direct the optimization of high-cost computational resources, such as GPU clusters and cloud instances, balancing high-performance training needs with fiscal responsibility.
Recruit and Mentor Top Talent: Build and lead a high-performing team of ML Infra and DevOps engineers, providing technical coaching, career pathing, and performance management.
Establish MLOps Standards: Drive the adoption of best practices in CI/CD for ML, Infrastructure as Code (IaC), and automated testing to ensure a modular and maintainable system.
Bridge the Research-Engineering Gap: Act as the primary cross-functional lead, translating the complex needs of AI Researchers into actionable engineering requirements for the infrastructure team.
Define and Track Velocity Metrics: Establish KPIs for the infrastructure team, such as model deployment frequency, mean time to recovery (MTTR), and infrastructure cost per inference.
Champion Operational Excellence: Lead root-cause analyses (RCAs) for production failures and foster a culture of accountability where systemic fixes are prioritized over "quick patches."
Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.

Qualifications

Required:

10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.
Proven track record of building and scaling MLOps platforms that support the full model lifecycle—from data ingestion and distributed training to real-time inference and monitoring.
Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).
Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.
Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.
Experience partnering with AI Research and Data Science teams to understand their unique workflows and translate research needs into robust, scalable engineering solutions.
Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.
Demonstrated ability to manage high-cost compute resources, with experience optimizing GPU utilization and cloud spend in a hyper-growth environment.
Excellence in operational leadership, with a history of driving service availability, performance, and stability through rigorous on-call rotations and root-cause analysis.
A product-oriented mindset, with the ability to treat infrastructure as a platform and prioritize the roadmap based on researcher velocity and business ROI.
Exceptional communication and influence skills, capable of navigating ambiguity and building consensus across engineering, product, and data science leadership.
Kindness and high standards: You move fast and push for excellence, but you do so as a supportive team player who fosters a culture of psychological safety and extreme candor.

All posted ranges are reflective of base salary and may vary depending upon experience level and location. Bonus and equity may also be provided for eligible roles.

Pay Range

$268,000—$300,000 USD

WHY QUINCE?

Joining Quince means being part of a mission-driven team reshaping retail. You will work alongside talented colleagues, tackle meaningful challenges, and contribute to building a more sustainable, accessible future for customers and partners alike.

EQUAL OPPORTUNITY & HIRING INTEGRITY

Quince provides equal employment opportunities to all employees and applications for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

Quince is committed to providing reasonable accommodations to qualified individuals with disabilities. If you need a reasonable accommodation to complete your application or to perform the essential functions of a role at Quince, please let us know by completing this accommodation form. We review all requests individually and will work with you to determine appropriate accommodations on a case-by-case basis.

Employment is contingent upon successful completion of a background check. Quince will conduct background checks in compliance with applicable federal, state, and local laws.

Security Advisory: Beware of Frauds

At Quince, we're dedicated to recruiting top talent who share our drive for innovation. To safeguard candidates, Quince emphasizes legitimate recruitment practices. Initial communication is primarily via official Quince email addresses and LinkedIn; beware of deviations. Personal data and sensitive information will not be solicited during the application phase. Interviews are conducted via phone, in person, or through the approved platforms Google Meets or Zoom—never via messaging apps or other calling services. Offers are merit-based, communicated verbally, and followed up in writing. If personal information is requested to initiate the hiring process, rest assured it will be through secure and protected means.