Senior Staff Software Engineer

RBGlobal•Westchester, IL

23h

About The Position

The Senior Staff Engineer — Data Science will serve as a technical leader in advancing IAA’s Machine Learning (ML) and advanced analytics capabilities. This role involves architecting and building sophisticated ML systems, including supervised, unsupervised, and deep learning models, as well as AI agentic applications, with a strong business orientation and a proven track record of delivering high-impact, data-driven solutions at scale. The Senior Staff Engineer will drive the technical vision for machine learning across the organization, implementing scalable solutions that deliver measurable growth and operational efficiency. Working closely with cross-functional teams within the IAA ecosystem, the Senior Staff Engineer will lead the integration of ML and AI solutions into enterprise-scale services. This role requires deep expertise in data best practices and system design to ensure the most appropriate data sources, architectures, and approaches are leveraged to solve complex business problems across customer behavior, vehicle listings, and other business-critical domains.

Requirements

8+ years of experience building and deploying production machine learning systems, including supervised, unsupervised, and deep learning approaches
Advanced proficiency in Python and SQL, with deep experience in ML libraries such as scikit-learn, PyTorch or TensorFlow, pandas, and NumPy
Strong hands-on experience in Natural Language Processing (NLP) and computer vision applications
Hands-on experience building AI agentic applications using LangChain, LangGraph, or similar frameworks, including integration with LLMs and external tools/APIs
Hands-on experience with the full ML lifecycle: feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoring
Strong foundation in statistics and core ML algorithms (gradient boosting, neural networks, clustering, dimensionality reduction)
Experience architecting ML solutions on large-scale datasets using distributed computing frameworks (Spark, Dask) and cloud platforms (AWS, Azure, or GCP)
Demonstrated ability to set technical direction, influence cross-functional roadmaps, and communicate complex technical strategies to executive stakeholders
Track record of mentoring senior data scientists and raising the technical bar across a data science organization

Nice To Haves

Experience with Model Context Protocol (MCP) for building interoperable AI agent integrations across tools, data sources, and enterprise systems
Experience designing and orchestrating multi-agent AI systems, including tool-use agents, retrieval-augmented generation (RAG) pipelines, and autonomous decision-making workflows
Familiarity with Large Language Models (LLMs), prompt engineering, and Generative AI techniques
Experience with MLOps practices and CI/CD pipelines for machine learning workflows
Experience with real-time inference systems and low-latency model serving
Contributions to open-source projects

Responsibilities

Define and drive the technical roadmap for data science and ML initiatives across the organization
Architect and build end-to-end ML systems, from problem framing and data strategy through model development, deployment, and monitoring at scale
Design and build advanced supervised, unsupervised, and deep learning models, including NLP and computer vision solutions, to solve high-impact business problems
Develop AI agentic applications and LLM-powered solutions to automate workflows and unlock new capabilities
Perform feature engineering, data validation, and quality assurance across large, complex datasets
Partner with data engineering, ML platform, and software engineering teams to productionize models and ensure scalability, reliability, and monitoring
Translate ambiguous, cross-functional business challenges into well-scoped technical strategies and communicate findings to executive and non-technical stakeholders
Mentor and elevate staff and senior data scientists, and establish best practices, standards, and technical direction across the data science team