Machine Learning Infrastructure Engineer

Role

We’re looking for builders–intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You’ll design and scale the core infrastructure that powers machine learning and self-hosted large language model applications across the company, working side by side with machine learning scientists to bring cutting-edge models into production and unlock entirely new product experiences.

What You'll Do

• 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.

People Who Do Well at Whatnot

• Comfortable figuring things out as they go.
• Biased toward action.
• Genuinely curious about what they're building.
• Care more about outcomes than credit.
• Stay close to the product and the people using it.

Qualifications

• 4+ 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.
• 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.