Applied AI Scientist: Agentic Systems & Forecasting

XPO Logistics, Inc.; Boston (MA); On-site; USD 100,000 - 120,000; Full time

Benefits offered by this job

Full health insurance

401(k) with company match

Paid time off

Job summary

XPO Logistics, Inc. is seeking an Applied AI Scientist in Boston to design agentic experimentation layers over optimization models and develop evaluation harnesses to benchmark model performance before promotion to production.

The role emphasizes partnering with OR scientists to translate model internals into meaningful improvements and scaling automated experimentation infrastructure. The position requires strong Python and ML framework skills, experience with time-series forecasting, and the

Qualifications

• Bachelor’s degree or equivalent related work or military experience.
• 1 year of experience designing evaluation harnesses or benchmarks to rigorously assess model or agent performance against existing baselines.
• Hands-on experience building applied AI systems, including one or more of: agent-based/agentic systems, experimentation frameworks, or applying LLM-based/foundation model architectures to time-series forecasting problems.
• Proficiency in Python and modern ML/AI frameworks and platforms (e.g. PyTorch, HuggingFace).
• Strong communication skills, with the ability to explain AI system behavior and tradeoffs to technical and business stakeholders, and to collaborate closely with optimization/OR scientists on what constitutes a meaningful model improvement.

Responsibilities

• Design and build agentic experimentation layer over optimization models developed by the team's OR/data scientists, including proposing variants, running evaluations, and surfacing promising results.
• Build evaluation harnesses that rigorously and automatically benchmark model and agent performance against existing baselines before promotion to production.
• Implement operational safeguards for autonomous experimentation systems, such as automated regression checks, compute/cost limits, and human-in-the-loop gates before production promotion.
• Evaluate and integrate modern LLM-based and foundation model architectures (e.g., Chronos-style time-series models) for ETA prediction and demand forecasting for pickup prediction.
• Partner closely with the team's optimization/OR scientists to understand model internals, solver behavior, and what constitutes a meaningful improvement for P&D use cases.
• Partner with machine learning engineers on the underlying infrastructure needed to run automated experimentation and evaluation at scale.
• Communicate technical approaches and tradeoffs to both technical and business audiences.
• Stay current on advances in agentic systems, time-series foundation models, and applied GenAI to guide adoption at XPO

Skills

Python

PyTorch

HuggingFace

Evaluation harnesses

Applied AI systems

Agent-based systems

Time-series forecasting

Communication

Education

Bachelor's degree or equivalent

Master's degree or PhD in relevant field

Tools

PyTorch

HuggingFace

Job description

XPO Logistics, Inc. is seeking an Applied AI Scientist in Boston to design agentic experimentation layers over optimization models and develop evaluation harnesses to benchmark model performance before promotion to production.

The role emphasizes partnering with OR scientists to translate model internals into meaningful improvements and scaling automated experimentation infrastructure. The position requires strong Python and ML framework skills, experience with time-series forecasting, and the