Staff Engineer - ML Infra / MLOps

Quince; Palo Alto (CA); On-site; USD 218,000 - 285,000; Full time

Job summary

Quince is seeking a Staff ML Engineer to join its growing ML Infra team in Palo Alto. You will architect and operate production-grade ML systems, from distributed training pipelines to high-throughput inference, ensuring extensibility and observability.

You will lead the developer experience for data scientists, drive CI/CD for ML, and mentor engineers while optimizing GPU usage and cost. A startup mindset with autonomy is essential.

Qualifications

• 8+ years in industry, with at least 4+ years in ML Infrastructure/MLOps or large-scale data platforms.
• Proven track record building MLOps platforms spanning data ingestion, distributed training, real-time inference, and governance.
• Deep expertise in cloud-native infra (AWS preferred), Kubernetes (EKS), Docker, and IaC tools (Terraform/Pulumi).
• Hands-on with ML frameworks (PyTorch, TensorFlow) and feature stores; strong opinions on developer experience for ML teams.

Responsibilities

• Architect and own end-to-end ML infrastructure design (training, serving, feature pipelines, monitoring) for modular, scalable platforms.
• Build a productive developer experience enabling data scientists to move from idea to production with minimal friction.
• Set and enforce ML CI/CD, IaC, model versioning, experiments, and deployment strategies (blue-green, canary).
• Lead platform evaluation and component selection (inference runtimes, feature stores, orchestration).
• Optimize GPU utilization, batching, and cloud costs for training and inference workloads.
• Ensure production scalability and reliability with robust monitoring, alerting, and automated recovery.
• Mentor junior engineers through reviews, pairing, and design discussions to raise the technical bar.
• Lead RCAs for production issues and drive systemic, permanent fixes with strong on-call discipline.

Skills

ML Infrastructure

MLOps

AWS

Kubernetes

Docker

Terraform/Pulumi

PyTorch/TensorFlow

Feature Stores

CI/CD for ML

On-call discipline

Tools

Kubeflow

SageMaker

Spark

Kafka

Job description

Founded in 2018, Quince was built to challenge the idea that nice things have to cost a lot. Our mission is simple: to make really high quality essentials for really low prices, produced fairly and sustainably. We believe everyone deserves exceptional craftsmanship and timeless design without the traditional markups. Quince is a direct-to-consumer (DTC) model that cuts out middlemen and leverages just-in-time manufacturing to minimize waste and maximize value.

Quince is a tech company disrupting the retail industry by putting AI, analytics and automation at the center of everything we do. Our unwavering commitment to excellence and company values guide our teams and actions:

Customer First: We prioritize customer satisfaction in every decision.

High Quality: True quality means premium materials and rigorous production standards you can feel good about.

Essential Design: We focus on timeless, functional essentials instead of chasing trends.

Always a Better Deal: Innovation and transparency ensure value for both customers and partners.

Social & Environmental Responsibility: We commit to sustainable materials, ethical production, and fair wages.

Quince partners with world-class manufacturers across the globe and serves millions of customers. With strong investor backing and a focus on sustainable growth, we are a company that is rapidly scaling while maintaining a commitment to quality, simplicity, and radical price transparency.

OUR TEAM AND SUCCESS

At Quince, you will be part of a high-performing team that is redefining what quality, value, and sustainability mean in modern retail. We are a destination for builders, innovators, and operators to come together and challenge the status quo. Our collective ambition is bold. We are creating an entirely new category and customer experience – one that democratizes luxury and provides high quality products at radically low prices. That mission demands a world-class team committed to excellence.

If you are motivated by impact, growth, and purpose, you will find a strong sense of belonging at Quince.

THE ROLE

Staff Engineer for ML Infra / MLOps

We are seeking a Staff ML Engineer t o join our growing team.

The ideal candidate is a deeply technical ML infrastructure engineer who combines hands‑on mastery with system‑level thinking. You have built and operated production‑grade ML systems at scale — from distributed training pipelines and feature stores to high‑throughput inference serving — and you take pride in engineering platforms that other engineers love to use. You don’t just build for today’s requirements; you design for extensibility, observability, and resilience.

You are the kind of engineer who gravitates toward the hardest problems — whether that’s optimizing GPU utilization at the tail of the cost curve, designing a zero‑downtime model deployment system, or defining the architectural patterns that will define how Quince industrializes AI at scale. You operate with high autonomy, hold yourself to exceptional standards, and elevate the engineers around you through code reviews, technical mentorship, and by setting a bar for what great looks like.

Responsibilities

• Architect the ML Infrastructure Foundation: Own the end-to-end technical design of Quince’s ML platform — including model training, serving, feature pipelines, and monitoring — ensuring it is modular, scalable, and built for long‑term extensibility.
• Build the “Paved Road” for Production: Design and implement the core developer experience for Quince’s Data Scientists and AI Researchers, enabling them to move from “idea to production” with minimal friction and maximum reliability.
• Drive Technical Excellence Across the Stack: Set and uphold engineering standards in CI/CD for ML, Infrastructure as Code (IaC), model versioning, experiment tracking, and deployment strategies (blue‑green, canary) — and build the tooling that makes those standards the path of least resistance.
• Own High-Impact System Design Decisions: Lead the technical evaluation and selection of core platform components — from inference runtimes and feature stores to orchestration frameworks — with a clear‑eyed view of build vs. buy tradeoffs.
• Optimize Compute Performance & Cost: Design and implement GPU utilization optimizations, model batching strategies, and cloud cost controls to maximize performance per dollar across training and inference workloads.
• Ensure Production Scalability & Reliability: Architect ML serving infrastructure that gracefully handles traffic surges, seasonal spikes, and model version transitions, with robust monitoring, alerting, and automated recovery.
• Mentor and Elevate the Engineering Team: Provide deep technical mentorship to junior and mid‑level engineers through design reviews, code reviews, and pairing sessions — raising the collective technical bar without adding process overhead.
• Champion Operational Excellence: Lead root‑cause analyses (RCAs) for production failures and drive systemic, permanent fixes over reactive patches. Model a culture of rigorous on‑call discipline and accountability.

Qualifications

• 8+ years of industry experience, with at least 4+ years of focused, hands‑on work in ML Infrastructure, MLOps, or large‑scale Data Platform engineering.
• Proven track record of designing and building MLOps platforms that support the full model lifecycle — from data ingestion and distributed training to real‑time inference and model governance.
• Deep expertise in cloud‑native infrastructure (preferably AWS), Kubernetes (EKS), Docker, and Infrastructure as Code tools (Terraform/Pulumi).
• Hands‑on mastery of ML frameworks such as PyTorch, TensorFlow, Kubeflow, or SageMaker, with strong opinions on building a cohesive, high‑leverage developer experience.
• Expertise in building Feature Stores and high‑throughput data pipelines (Spark, Flink, Kafka), with a strong understanding of training/serving skew and data consistency.
• Expert‑level knowledge of CI/CD for ML, including model versioning, experiment tracking, and deployment strategies such as blue‑green and canary rollouts.
• Demonstrated ability to optimize GPU utilization, implement model batching, and systematically reduce cloud infrastructure costs.
• Strong operational instincts, with a history of improving reliability through rigorous on‑call practices, proactive monitoring, and root‑cause analysis.
• You understand the hustle of a startup and are good at handling ambiguity. You are a curious, quick learner who loves to experiment and thrives at a rapid pace.

Pay Range: $218,000-$285.000 (base) + bonus and stock

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

$218,000 - $285,000 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.