Software Engineer, Core Machine Learning
New
W
WhatnotLive commerce
Team members in this role must live within commuting distance of our Seattle or San Francisco hub.Full-TimeMiddle
Salary$180,000 - $300,000/year + benefits + stock options
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Job Details
- Experience
- 4+ years of professional experience; 2+ years of software engineering experience building and maintaining production systems at consumer scale; 2+ years of hands-on experience training and deploying traditional ML models in production.
- Required Skills
- PostgreSQLDynamoDBElasticSearchMachine LearningPyTorchGrafanaRedisDatadogscikit-learn
Requirements
- Have 4+ years of professional experience.
- Have a bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related technical field, or equivalent work experience.
- Have 2+ years of software engineering experience building and maintaining production systems at consumer scale.
- Have 2+ years of hands-on experience training and deploying traditional machine learning models in production, such as classification, ranking, or regression models.
- Have experience with PyTorch, LightGBM, or scikit-learn.
- Be able to work autonomously and drive initiatives across multiple product areas.
- Have experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, or Redis.
- Have experience with visualization tools for monitoring and logging, such as DataDog or Grafana.
- Be familiar with cloud computing platforms and managed services such as AWS, Kubernetes, SageMaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, or Flink.
- Be able to collaborate in a remote working environment and produce well-tested, reproducible work.
- Have documentation and communication skills, including communicating findings with leadership and product teams.
Responsibilities
- Develop and deploy production machine learning algorithms and core infrastructure for buyer and seller experiences.
- Contribute to and set direction across opportunity sizing, feature engineering, model training, evaluation, load testing, deployment, and alerting.
- Design and implement supporting infrastructure, including streaming pipelines, async worker queues, event enrichment, continuous training loops, and model monitoring systems.
- Build LLM tool access controls and dynamic agent evaluation frameworks.
- Define project roadmaps that deliver value quickly while building scalable, extensible systems.
- Turn experimental ideas into scalable, well-tested, monitored production systems.
- Communicate tradeoffs and findings to product teams and leadership.
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