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# Software Engineer

- **Company:** Whatnot Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $180,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Elastic Compute Cloud, Amazon S3, Databases, Data Visualization, Amazon DynamoDB, Elasticsearch, PostgreSQL, Load Testing, Machine Learning, Redis, Software Engineering, Software Systems, Datadog, Data Logging, Cloud Platform System, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Grafana, Information Technology, Apache Flink, Xgboost, Apache Kafka, Functional Programming, Data Pipelines, AWS EKS
- **Published:** October 15, 2026
- **Apply:** https://startup.jobs/software-engineer-core-machine-learning-whatnot-com-8731185

## About the Role

As our next Software Engineer, Core Machine Learning you should have 4+ years of professional experience, plus:

 * Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related technical field, or equivalent work 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 (e.g. classification, ranking, or regression models using PyTorch, LightGBM, or scikit-learn)
 * 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 Kubernetes, 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.

## Description

We're looking for entrepreneurial software engineers who thrive on ambiguous, 0-to-1 work. You'll design and deploy both traditional ML models and infrastructure (contextual bandits to manage ad spend, shipment ETA predictions) and LLM-powered systems (RAG pipelines, human-in-the-loop evaluation frameworks) to address the business's most pressing problems. Your job will change every ~12 months as we hand off stable products and pivot to address the business's next big need. Strong applicants display exceptional business judgement and are equally comfortable contributing to production infrastructure and training the models that run on it. They build for the long term while finding ways to aggressively pull forward business impact.

What you'll do:

 * Develop and deploy new both production machine learning algorithms and core infrastructure to enrich the buyer and seller experience.
 * Contribute to and set direction across the entire machine learning development process: opportunity sizing, feature engineering, model training, evaluation, load testing, deployment, and alerting.
 * Design and implement supporting software infrastructure to unblock your work: streaming pipelines, async worker queues, event enrichment infrastructure, continuous training loops, model monitoring systems, LLM tool access controls, and dynamic agent evaluation framework to give just a few examples
 * Define project roadmaps that deliver value quickly while building towards scalable, extensible long-term systems
 * Bridge research and production: take an experimental idea and harden it into a scalable, well-tested, monitored system.
 * Communicate tradeoffs and findings clearly to product and leadership.

We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our Seattle or San Francisco hub.

You

Curious about who thrives at Whatnot? We've found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.