Staff Engineer, Quince
ML Infra / MLOps
$218-285k
+Bonus and Stock Options
Office located in Palo Alto, CA
Affordable luxury fashion & home goods
Be an early applicant
Affordable luxury fashion & home goods
1001+ employees
Be an early applicant
$218-285k
+Bonus and Stock Options
Office located in Palo Alto, CA
1001+ employees
Company mission
To create products of equal or greater quality than the leading luxury brands at a much lower price.
Role
Who you are
- The ideal candidate is a deeply technical ML infrastructure engineer who combines hands-on mastery with system-level thinking
- 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
What the job involves
- We are seeking a Staff ML Engineer to join our growing team
- 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
- 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
Our take
Recently, there has been more widespread awareness of the alarming waste of resources and environmental impact caused by the fast fashion industry. Recognizing these concerns, Quince is looking to offer a more sustainable alternative to consumers.
Quince is a direct-to-consumer brand that offers high-quality products at lower prices by cutting out the middlemen. The company provides wardrobe staples and home goods, with an emphasis on minimalism, simplicity and timeless designs.
The company works directly with factories to produce inventory on a near-just-in-time basis. By eliminating unnecessary intermediaries, Quince can streamline the supply chain and reduce financial and environmental waste.
This approach allows Quince to ship goods directly to consumers' doorsteps, providing a more efficient and sustainable shopping experience. This has proven very popular, allowing the company to raise significant funding, reach a valuation of $4.5 billion and expand to Canada.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
58% employee growth in 12 months
Company
Funding (last 2 of 7 rounds)
Mar 2026
$500m
SERIES E
Jul 2025
$200m
LATE VC
Total funding: $958m
Company HQ
SoMa, San Francisco, CA
Leadership
Sid Gupta
(CEO & Co-founder)Graduated from Stanford University with an MBA in Finance. Previously the Founder and CEO of Lolli and Pops as well as the Director of Strategy at TechTurn.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 30 more jobs at Quince