> Markdown version of [/jobs/ext/1238929-machine-learning-platform-engineer](https://www.wearedevelopers.com/jobs/ext/1238929-machine-learning-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Platform Engineer - **Company:** Whatnot Inc. - **Location:** New York, NY, United States (Remote available) - **Salary:** $245,000.0 - $345,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Databases, Data Visualization, Distributed Computing Environment, Amazon DynamoDB, Elasticsearch, Python (Programming Language), PostgreSQL, Machine Learning, Redis, Azure Machine Learning, Software Engineering, Datadog, Data Logging, Graphics Processing Unit (GPU), Cloud Platform System, Grafana, Information Technology, Low Latency, Apache Flink, Apache Kafka, Machine Learning Operations, Functional Programming - **Published:** July 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f9cc452d9eb90058 ## About the Role As our next AI/ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus: * 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. ## Description 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., * 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. US Based: 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 New York, Seattle, Los Angeles, and San Francisco hubs. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) - [Software Engineering Social Connection: Yubo’s lean approach to scaling an 80M-user infrastructure](https://www.wearedevelopers.com/videos/1583-software-engineering-social-connection-yubo-s-lean-approach-to-scaling-an-80m-user-infrastructure) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)