Lead the technical strategy for reinforcement learning, post-training, and tool-using LLM agents within the AI Studio, focusing on training LLM-based agents to operate supply chain software.
Responsibilities
Lead the technical strategy for reinforcement learning, post-training, and tool-using LLM agents within the AI Studio.
Build and manage a team of machine learning engineers working on agent training, RL environments, reward modeling, evaluation, data generation, and training infrastructure.
Design environments where LLM agents learn to operate Blue Yonder software through APIs, tools, workflows, simulations, and human feedback.
Develop training and evaluation systems for multi-step supply chain workflows across planning, warehouse management, transportation, commerce, and network operations.
Define what "good" looks like for operational agents: correct tool use, constraint adherence, business outcome quality, latency, cost, robustness, escalation behavior, and human trust.
An ambitious ML Engineer focused on LLMs, agents, and reinforcement learning to help build the training, evaluation, and tooling systems behind robust AI decision-making products.
Responsibilities
Design and implement LLM-powered agent environments for supply chain decision-making
Fine-tune, adapt, and evaluate LLMs for domain-specific reasoning and decision support
Design, test, and iterate on reward functions that capture the behaviors we want from LLM agents
Review LLM traces and rollouts to understand model reasoning, failure modes, reward hacking, and shortcut behaviour
Identify when an LLM is exploiting the reward function, escaping the intended RL process, or optimizing for proxy metrics instead of the real objective
Requirements
Nice to have: human-in-the-loop ML systems
Nice to have: evaluation frameworks for open-ended tasks
Apply and adapt machine-learning and reinforcement-learning research to OD’s product roadmap, translating research direction into shippable capability.
Design, train and evaluate models using reinforcement learning, RLHF and multi-objective policy optimization, balancing competing objectives such as accuracy, safety, latency and cost.
Work on LLM post-training: fine-tuning, alignment, reward modelling and rigorous evaluation.
Prototype fast, then work with the engineering teams to harden what works into production.
Define how frontier models can be evaluated and how decision making can be audited.
Requirements
PhD in AI, machine learning or a closely related field
Postdoctoral research position in Machine Learning
Nice to have: Computer vision
Nice to have: Open-source contributions
Nice to have: Applied research that shipped into a product
Research Scientist focused on large language models and diffusion architectures, developing post‑training techniques and deploying innovations into production.
Research and develop audio-visual interactive and generative models, such as any-to-any models across speech, text, and video, real-time agentic interactive systems, and autoregressive generation.
Improve inference speed and quality through distillation, streaming generation, and efficient architectures.
Develop post-training methods, including fine-tuning and multimodal reinforcement learning.
Build evaluation methods and benchmarks for long-form, multi-turn interaction and generation.
Work with researchers and engineers to bring results into prototypes, and publish findings at peer-reviewed venues.
Requirements
PhD in Computer Science, Electrical Engineering, or a related field
The Centre for Quantum Technologies (CQT) at Nanyang Technological University (NTU) invites applications for a Research Fellow position in the area of Quantum-Enhanced Learning, Agents and Algorithms.
Responsibilities
Conduct independent and collaborative research in quantum information science, machine learning, and related fields.
Develop novel theoretical frameworks, algorithms, and models for quantum-enhanced learning and intelligent agents.
Publish research outcomes in reputable peer-reviewed journals and present findings at scientific conferences.
Contribute to the preparation of research proposals, grant applications, and technical reports.
Collaborate with researchers from diverse disciplinary backgrounds to advance research objectives.
Requirements
PhD in Quantum Information, Theoretical Physics, Computer Science, Mathematics, Machine Learning, or a closely related discipline.
Nice to have: Quantum algorithms and quantum computation
Nice to have: Machine learning, statistical learning theory, or reinforcement learning
Nice to have: Quantum information theory and quantum stochastic processes
Nice to have: Quantum resource theories and quantum thermodynamics
Lead research and development of AI and machine learning solutions, focusing on tokenomics, routing policies, and energy-aware deployment across edge-to-cloud environments.
Compensation
The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level. – United States of America: Annual Salary USD 143,000 - 290,500 in California
Responsibilities
Participate as a member of the Hewlett Packard Labs Machine Learning research team
Research and prototype methods for job and system state representations
Develop routing policies, model cascades, and adaptive controllers
Assign and enforce token, context, reasoning, and compute budgets
Build benchmark, telemetry, accounting, and workload-simulation environments
Requirements
PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or related field
Our Mission
Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.
What you'll need
MS/PhD or equivalent industry experience in robotics, computer science, or related fields
2+ years of experience in research and development
Ability to demonstrate technical proficiency in Reinforcement Learning, Control, Robotics, or Imitation Learning
Experience with sim-to-real for robotic hardware specifically legged robots or highly dynamic mobile vehicles.
Experience in working with perception-based control
Advanced programming skills in Python or C++
Expertise with deep learning frameworks such as PyTorch and robotic simulation
Bonus
Experience in any of the following is a plus; none is required.
Knowledge of Model Predictive Control
Knowledge in robot perception
Experience in combining model-based and data-driven approaches
Experience in working with Isaac-Lab
Experience in working with ROS or ROS2
Experience with Docker, cloud computing, or similar applications
Experience with parallel programming (e.g., CUDA)
Proven track record in top-tier conferences and journals in Machine Learning, Robotics, Control, or related fields
These attributes are great to have but not required. Candidates who lack these should not be discouraged from applying.
We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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