Software Engineer, Autonomy

Introduction

The Autonomy Behavior ML Data Optimization team is seeking a Software Engineer with strong data processing and pipeline engineering skills. The role involves building, scaling, and optimizing ScenarioScout, a scenario discovery platform that transforms large-scale driving data into actionable insights. This tool empowers teams across the autonomous vehicle development lifecycle by enabling fast, semantic similarity search over millions of driving scenario embeddings.

Required Skills & Qualifications

• 3 years of professional software engineering experience with a focus on data processing and pipeline engineering.
• Experience designing, building, and optimizing distributed data processing pipelines at scale using technologies like Spark, Databricks, AWS EMR, AWS Batch, or Ray Core/Data.
• Strong proficiency in Python with experience building production data pipelines and web services (FastAPI, Uvicorn, or similar async frameworks).
• Experience building and maintaining data visualization dashboards, with proficiency in SQL, and familiarity with PySpark/Scala for large-scale data manipulation.
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster, or similar) for managing complex multi-step data processing pipelines.
• Applicants must be able to work directly for Artech on W2.

Preferred Skills & Qualifications

• Machine Learning concepts, particularly embeddings, clustering (k-means), and similarity search.
• Building or operating ML serving infrastructure (e.g., TensorFlow Serving, TorchServe, Triton, or custom model serving like RayServe).
• Full-stack development with experience owning applications end-to-end from frontend to infrastructure.

Day-to-Day Responsibilities

• Build and maintain the Python/FastAPI backend powering high-throughput embedding search, bulk execution APIs, and dataset management endpoints.
• Design, build, and maintain Airflow DAGs that orchestrate end-to-end dataset creation and refresh pipelines, including embedding generation, distributed k-means clustering, FAISS index building, embedding cache construction, and dataset statistics computation.
• Collaborate with ML researchers on integrating new embedding types, improving embedding quality, and exploring LLM-powered natural language querying capabilities.

For immediate consideration please click APPLY to begin the screening process with Alex.