Machine Learning Platform Engineer
New
W
WhatnotMachine learning
US Based: We offer flexibility to work from home or from one of our global office hubs. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.Full-TimeSenior
Salary$245,000 - $345,000/year + benefits + stock options
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Job Details
- Experience
- 7+ years of professional experience developing machine learning systems and algorithms; 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads; 1+ years of professional experience developing software in Python.
- Required Skills
- AWSPostgreSQLPythonDynamoDBElasticSearchMachine LearningApache KafkaGrafanaRedisDatadog
Requirements
- Have 7+ years of professional experience developing machine learning systems and algorithms.
- Have 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads.
- Have 1+ years of professional experience developing software in Python.
- Hold a bachelor’s degree in Computer Science, Statistics, Applied Mathematics, or a related technical field, or have equivalent work experience.
- Have experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, and Redis.
- Have familiarity with monitoring and logging visualization tools such as DataDog and Grafana.
- Be familiar with cloud computing platforms and managed services such as AWS SageMaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, and Flink.
- Be able to work autonomously and drive initiatives across multiple product areas.
- Be able to communicate findings with leadership and product teams.
- Have experience collaborating in a remote working environment and producing well-tested, reproducible work.
- Have documentation and communication skills.
Responsibilities
- Own infrastructure powering AI and ML models across critical business surfaces, including growth, recommendations, trust and safety, fraud, and seller tooling.
- Prototype, deploy, and productionalize novel ML architectures.
- Design and scale inference infrastructure to serve large models with low latency and high throughput.
- Build distributed training and inference pipelines using GPUs and model and data parallelism.
- Take on new technical challenges as AI scales across Whatnot’s ecosystem.
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