Machine Learning Platform Engineer

Whatnot is hiring a Machine Learning Platform Engineer to build and scale infrastructure powering machine learning and self-hosted large language model applications. The role focuses on productionizing ML models, large-model serving, and distributed GPU training and inference across the company.

Responsibilities:

• Own the infrastructure powering AI and ML models across critical business surfaces–supporting growth, recommendations, trust and safety, fraud, seller tooling, and more
• Prototype, deploy, and productionalize novel ML architectures that directly shape user experience and marketplace dynamics
• Design and scale inference infrastructure capable of serving large models with low latency and high throughput
• Build distributed training and inference pipelines leveraging GPUs and both model and data parallelism
• Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot’s ecosystem

Requirements:

• 7+ years of professional experience developing machine learning systems and algorithms
• Bachelor's degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience
• 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads
• 1+ years of professional experience developing software in Python
• Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams
• Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis
• Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana
• Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink
• Professionalism around collaborating in a remote working environment and well tested, reproducible work
• Exceptional documentation and communication skills