Senior Software Engineer, ML Platform

Stack AV•Pittsburgh, PA

About The Position

Stack is developing AI and autonomous systems for the trucking transportation industry, combining artificial intelligence, robotics, machine learning, and cloud technologies. The ML Data Understanding team provides trusted data for Stack’s machine learning applications across data mining, training, and safety evaluation, collaborating with autonomy teams on data engineering, mining, modeling, and infrastructure. In this role, you will help set the direction for and build platforms and services for multimodal semantic search, data discovery, data mining, and dataset management. The work includes applying large language models and other AI techniques, developing data workflows and infrastructure for training and inference, and delivering solutions that span onboard and offboard systems to support autonomous vehicle product development.

Requirements

At least 5 years of relevant domain experience.
Experience with both machine learning platforms and machine-learning-based applications.
A proven track record of building scalable, reliable infrastructure in a fast-paced environment.
Deep understanding of design trade-offs and the ability to explain them and achieve alignment with others.
Hands-on experience developing semantic search infrastructure, generating multimodal video, image, or audio embeddings, and managing vector databases.
Experience with multimodal data indexing, model training infrastructure, model optimization, or large data processing pipelines.

Nice To Haves

Experience building machine learning models or infrastructure in autonomous vehicles, perception, or decision-making domains.

Responsibilities

Architect multimodal semantic search and data discovery platforms for autonomous driving development using AI-assisted software engineering.
Investigate and apply large language models, visual language models, and Retrieval Augmented Generation techniques.
Build agentic data mining, semantic search, and data curation workflows as part of a recursive machine learning improvement loop.
Develop platform infrastructure for real-time querying and vector databases, as well as batch and stream processing using technologies such as Ray, Spark, and LanceDB or similar.
Deliver end-to-end data mining solutions spanning onboard C++ and offboard machine learning and data infrastructure.
Develop end-to-end real-time semantic search services for text, images, and videos, including vector database components.
Identify issues in existing machine learning infrastructure and proactively improve system performance.
Build low-latency, high-throughput batch or stream processing pipelines.
Drive technical discussions across multiple organizations and deliver solutions on time.
Write readable, high-performance Python and C++ code.