Machine Learning Platform Engineer

Whatnot•Los Angeles, CA

1d•$245,000 - $345,000•Remote

About The Position

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. This means building systems that make advanced ML dependable and fast at scale–from low-latency, large model serving to distributed training & high-throughput GPU inference.

Requirements

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
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.

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.

Benefits

Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
16 weeks of paid parental leave + one month gradual return to work company leave allowances run concurrently with country leave requirements which take precedence.