Please note that the following enhanced screening and interview requirements apply to this role: Virtual backgrounds or headphones / earbuds are not permitted during web-based interviews. Additionally, a minimum of one onsite, in person interview will be required as part of the application process. By choosing to apply, you acknowledge and agree to these requirements.What you'll need to succeed as an Applied AI Scientist at XPO:Minimum 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
Preferred qualifications:
- 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
About the Applied AI Scientist job:Pay, Benefits and more:
- 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
What you'll do on a typical day:
- 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.
