This role is focused on end-to-end ML system ownership, including data pipelines, feature engineering, model training, deployment, monitoring, and continuous optimization. You will lead the development of scalable ML platforms, drive best practices in MLOps, and enable reliable, high-performance model inference in both batch and real-time environments.
The ideal candidate combines strong software engineering expertise with deep ML knowledge and has experience building robust, scalable ML systems in production, including modern applications involving large language models (LLMs) and agent-based AI systems.What You'll Do
Architect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle management
Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, and inference
Develop and maintain high-performance model serving systems using APIs (e.g., FastAPI) for real-time and batch inference
Lead the design and implementation of feature stores and reusable feature pipelines across teams
Build and optimize distributed data processing workflows using Spark, Databricks, or similar platforms
Implement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment tracking
Design and manage model monitoring and observability frameworks to track performance, drift, latency, and system health
Drive strategies for model retraining, drift detection, and continuous improvement
Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
Contribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows
Ensure high standards of code quality, testing, documentation, and reproducibility
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
10+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systems
Strong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs)
Extensive experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
Proven experience designing and deploying scalable ML pipelines and services in production
Hands-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)
Strong experience with containerization (Docker) and orchestration platforms such as Kubernetes
Experience working with cloud platforms (GCP, AWS, or Azure) and building cloud-native ML solutions
Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining
Experience implementing CI/CD pipelines for ML workflows and managing version control systems (Git)
Strong experience with SQL and distributed data processing frameworks (e.g., Spark, PySpark)
Excellent problem-solving skills and ability to design scalable, maintainable systems
What We Do
In 1969, Don and Doris Fisher opened the first Gap store on Ocean Avenue in San Francisco. They wanted to make it easier to find a great pair of jeans, and they did. Their denim and records store was a hit, and it grew to become one of the world’s most iconic brands. Today we’re represented in more than 1400 stores in over 40 countries, and online. We have headquarters in New York, London, Shanghai, Tokyo, and, of course, San Francisco. Our unique aesthetic is optimistic cool, elevated American style. Our clothes are crafted with care, with focused attention to thoughtful design. We believe in staying true to our heritage while creating what’s next. Don and Doris Fisher always wanted to “do more than sell clothes.” They wanted to support the people who ran their company, to be active in their communities, and to have a positive impact on the world. Their vision helped transform retail, and we’re still following their lead. We stand for freedom and possibility for all; we champion diverse ideas that transcend generations, geographies and genders.
.png)
.png){kind=logo}
