AI Engineer- Experienced - On-site

Today’s logistics marketplace is an ever-changing landscape where you can make your mark. Spot gives you the tools to tackle industry challenges for our partners. Here, initiative, drive, and teamwork form the basis for a rewarding, fast-paced career.

About the Role:

In the AI Engineer role, you’ll build and scale intelligent systems that directly impact how freight is priced, quoted, and executed in real time. You’ll partner closely with data scientists and engineers to develop multi-agent AI solutions — powering internal tools that support our people and external capabilities that create a best-in-class digital customer experience.

You’ll play a critical role in advancing our pricing technology by deploying machine learning models and LLM-based agents on Databricks, Azure Foundry, and related cloud platforms. As our AI capabilities expand, you’ll refine the reliability, performance, and scalability of deployed systems to ensure they deliver accurate, fast insights to the business.

Performance, automation, and operational excellence matter — you will design monitoring and feedback loops to retrain models, detect drift, and continuously improve outcomes across Spot’s intelligent platforms.

• Develop and maintain machine learning models and agentic AI systems that improve pricing accuracy, quoting efficiency, and operational decision-making.
• Build and operate MLOps pipelines using Databricks, Delta Lake, and Azure tools to automate data preparation, model training, validation, deployment, and monitoring.
• Integrate ML and LLM-based agents with internal operational tools and customer-facing applications through APIs and orchestration frameworks.
• Implement observability for AI components: performance metrics, drift detection, automated retraining, and incident response.
• Collaborate with engineering teams on architecture that maximizes scalability, security, and throughput in production.
• Participate in code reviews and contribute to engineering standards for our growing AI ecosystem.
• Engage with business stakeholders to understand operational challenges and translate them into intelligent solutions.

• Four-year degree in Computer Science, Data Science, Engineering, or equivalent practical experience.
• 3–4+ years of applied machine learning experience with models deployed to production environments.
• Proficiency in Python and major ML frameworks (PyTorch, Tensor Flow, scikit-learn).
• Hands-on experience in Databricks (Databricks ML, Delta Tables, Model Serving, Unity Catalog).
• Practical knowledge of SQL, distributed data systems, and creating efficient data pipelines.
• Strong understanding of MLOps concepts including CI/CD for ML models, containerization, and versioned deployments.
• Ability to demonstrate capability in a guided technical exercise.

Skills:

• Passion for building real-world AI agents that deliver measurable business outcomes.
• Ability to thrive in a fast-paced, high-ownership engineering culture.
• Strong communication and collaboration skills with both technical and operational stakeholders.
• Organized, detail-oriented mindset with strong problem-solving instincts.
• Ability to prioritize and adapt in an environment of rapid innovation and changing needs.
• Confident decision-making backed by data and a continuous improvement mindset.

* This opportunity and direct referral are intended for Evans Scholars and members of the WGA/ESF network.

Apply through Spot's
online application . Once you apply, please email your resume to
Dan Puglisi (ESF Sr. Director, Career Services) for a direct referral.