About this role
Developing services, online and offline pipelines, and tools helps researchers and data scientists turn ideas into working measurements and iterate quickly. This team builds classification and evaluation systems that supply post-training and research teams with fast, reliable feedback regarding model behavior by combining machine learning and production engineering.
Ownership spans real-time systems, data engineering, model serving, and experimentation tools while maintaining careful attention to performance, reliability, and privacy. Engineers who possess deep software engineering expertise and enjoy building technical products in close partnership with users tend to thrive in this environment.
What you will do
- Design and build software services and APIs for configuring, running, and managing classifiers and evaluations.
- Build and operate online and offline classification pipelines that support reliable and efficient execution at scale.
- Improve model-serving performance across throughput, latency, concurrency, and compute utilization.
- Create tools that facilitate straightforward experimentation, debugging, and iteration for researchers and data scientists.
- Own production reliability, observability, and incident response while reducing the operational burden of the platform.
- Partner closely with machine learning engineers to bring new classification capabilities into production.
What you need
- Design, build, and operate software services or distributed systems at scale.
- Apply strong fundamentals in API design, concurrency, caching, performance, and failure recovery.
- Investigate complex production problems and make sound architectural tradeoffs.
- Demonstrate a track record of owning software from initial design through deployment and ongoing operation.
- Bring strong product judgment and an interest in working directly with technical users.
- Navigate ambiguous problems and maintain broad ownership across multiple parts of a system.
- Use AI agents fluently to move quickly across multiple workstreams in a fast-paced environment.
Nice to have
- Interest in machine learning, data science, and statistics
- Experience with model serving, ML platforms, or inference optimization
Benefits
- Offers Equity
Skills and tech stack
Questions candidates ask
What kind of team is this?
Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale.
What will I work on day-to-day?
You’ll develop services, online and offline pipelines, and tools that help researchers and data scientists turn an idea into a working measurement and iterate on it quickly. You’ll also shape the architecture, deliver new capabilities, improve inference efficiency, and keep the system dependable.
Who will I work with?
We work closely with researchers and data scientists and have broad ownership of the software that makes these capabilities available. You will partner with machine learning engineers to bring new classification capabilities into production.