Staff Software Engineer

Summary

Join Stack's ML Data team and contribute to building state-of-the-art infrastructure for machine learning training and inference workloads. The team's mission is to provide trusted data to power Stack's ML applications, working across data engineering, ML modeling, and ML infrastructure. You will play a key role in data mining, curation, annotation, and serving data for all ML needs. Responsibilities include building an inference service using LLMs and vector databases, driving auto-labeling initiatives, and developing data platform infrastructure for real-time querying and batch/stream processing. Success involves pushing GPU limits, building training loops for large models, shipping ML products at scale, and writing high-performance C++ code. The role requires experience with ML platforms, building scalable infrastructure, and collaboration across teams.

Requirements

• Experience with both ML platforms and building ML-based applications (modeling experience is a bonus)
• Proven track record of building scalable, reliable infrastructure in a fast-paced environment
• Ability to collaborate effectively across teams
• Experience building or using ML infrastructure for a large number of customer teams
• Deep understanding of design trade-offs with the ability to articulate those trade-offs and achieve alignment with others

Responsibilities

• Develop data platform infrastructure for real-time querying/vector databases and batch/stream processing using technologies like Ray, Spark, or similar
• Create Parquet-based object storage solutions (data lake/data warehouse)
• Build low latency/high throughput batch or stream processing pipelines
• Write (readable) high-performance C++ code
• Push the GPU to its limit from Python to CUDA kernel level
• Build the inference or training loop for large models, ideally with LLM flavor
• Ship ML products (NLP, computer vision, recommender systems, etc.) at scale to make a business impact

Preferred Qualifications

• Experience in building ML models or infrastructure in domains such as autonomous vehicles, perception, and decision-making (desirable but not required)
• Experience with model training, model optimization, or large data processing pipelines
• Prior experience in autonomous vehicles (AV) is a plus