Senior ML Infrastructure Engineer, Quince
MLOps
$200-250k
+ Bonus for eligible roles
Affordable luxury fashion & home goods
Job no longer available
Affordable luxury fashion & home goods
1001+ employees
Job no longer available
$200-250k
+ Bonus for eligible roles
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
- Bachelor degree in computer science, engineering or related field
- 5+ years of experience in ML Infrastructure or ML engineering
- Hands-on and expertise experience in
- Building and maintaining ML pipelines
- Building and managing scalable ML production infrastructure
- AWS or other major cloud services
- Strong knowledge of CI/CD practices for ML models
- Familiarity with DevOps principles and tools
- Familiarity with TensorFlow, PyTorch, or similar frameworks
- Proficient in Python and Java (or Scala)
- Excellent communication skills
- Move fast, be a team player, and kind
What the job involves
- We are seeking passionate individuals eager to revolutionize the way people purchase essential goods by leveraging cutting-edge ML and AI solutions
- Our centralized data science team is dedicated to optimizing and automating decision-making processes while delivering valuable, actionable business insights
- As an ML Infrastructure Engineer at Quince, you will play a critical role in shaping our ML development ecosystem
- You will build and own the foundational ML development processes, operational pipelines, and production infrastructure necessary to support a scalable, efficient, and impactful ML practice
- Your contributions will directly enhance our ability to drive meaningful business outcomes and innovation
- Design, Build, and Maintain ML Pipelines: Develop and optimize end-to-end machine learning pipelines, including data ingestion, model training, validation, deployment, and monitoring
- Implement Continuous Integration/Continuous Deployment (CI/CD) for ML Models: Establish robust CI/CD processes to automate the testing, deployment, and monitoring of machine learning models in production environments
- Build and Own Production Infrastructure for Serving ML Models: Design, deploy, and maintain the production infrastructure necessary for real-time and batch serving of machine learning models, ensuring high availability, scalability, and reliability
- Build and Own the Feature Store: Design, implement, and manage the feature store to ensure efficient and scalable storage, retrieval, and versioning of features used in machine learning models, enabling consistent and reusable feature engineering across teams
- Collaborate with Data Scientists and Engineers: Work closely with data scientists, data engineers, and software engineers to ensure seamless integration of ML models into production systems, aligning models with business goals
- Monitor and Optimize Model Performance: Implement monitoring solutions to track the performance of ML models in production, identifying and addressing any issues such as data drift, model degradation, or system bottlenecks
- Ensure Scalability and Reliability: Design and implement scalable and reliable ML infrastructure, leveraging cloud platforms, containerization, and orchestration tools like Kubernetes and Docker
- Automate Data and Model Management: Develop automated solutions for version control, model registry, and experiment tracking to manage the lifecycle of ML models efficiently
- Optimize Resource Utilization: Manage and optimize the use of computational resources, such as GPUs and cloud instances, to balance performance with cost-effectiveness
- Conduct Root Cause Analysis and Troubleshooting: Diagnose and resolve issues in ML pipelines, including debugging data, code, and model performance problems
- Document Processes and Systems: Create and maintain comprehensive documentation of ML pipelines, deployment processes, and operational workflows to ensure knowledge sharing and continuity
Application process
- 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
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.
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