Applied AI Scientist - Hybrid

• 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

Annual Salary Range: $100,000 to $120,000 Actual compensation may vary due to factors such as experience and skill set. This is an incentive-based position, which may include bonuses, incentive or commission plans.

• 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, * Bachelor’s degree in Computer Science, AI, Data Science, Engineering, or related field, or equivalent related work or military experience
• Master’s degree or PhD in Computer Science, AI, Machine Learning, Statistics, or related field
• 2+ years of experience building agentic systems for production use cases and/or R&D applications
• Experience designing operational safeguards (e.g., automated checks against regressions, runaway compute, or unvalidated models reaching production) for agent-based systems
• Practical experience applying time-series or tabular foundation models (e.g., Chronos or similar) to forecasting problems such as ETA prediction or demand forecasting
• Practical experience with foundation model fine-tuning or post-training techniques
• Practical experience applying reinforcement learning (e.g., RLHF, or RL for agent behavior and decision-making)
• Experience building retrieval-augmented generation (RAG) systems is a plus
• Experience applying agentic or applied AI techniques to logistics, transportation, or operations research domains

• Competitive compensation package
• Full health insurance benefits available on day one
• Life and disability insurance
• Earn up to 15 days of PTO over your first year
• 9 paid company holidays
• 401(k) option with company match
• Education assistance
• Opportunity to participate in a company incentive plan

XPO is a top ten global provider of transportation services, with a highly integrated network of people, technology and physical assets. At XPO, we look for employees who like a challenge and can communicate effectively in all situations. We want to leverage your skills and years of experience to drive positive results while ensuring a bright future for yourself and XPO. If you’re looking for a growth opportunity, join us at XPO.