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1–2 jobs· newest first

Senior Principal Machine Learning Engineer (Fulfilment)New
Grab · Beijing, China
On-site · Full-time · Principal
Director, Reinforcement Learning & Agentic Post-TrainingNew
Blue Yonder · Paris
Manager

+10K new in the last hour

Senior Principal Machine Learning Engineer (Fulfilment)

Grab · Posted Nov 10, 2026

Beijing, China
On-site
Full-time · Principal
10+ years

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Lead architect and technical lead for autonomous marketplace decisioning systems, guiding research-to-production ML/RL pipelines and generative AI tools.

Responsibilities

Architect and implement end-to-end reinforcement learning pipelines for pricing, dispatch, and supply repositioning
Design latent world models and spatio-temporal marketplace state representations
Establish off-policy evaluation frameworks and safety guardrails
Build behavioural simulation systems using fine-tuned LLMs
Partner with backend, streaming, and platform engineering teams to deploy low-latency decision agents

Requirements

PhD in Computer Science, Operations Research, Applied Mathematics, or related quantitative discipline
Master’s degree with 12+ years of equivalent experience

Skills

reinforcement learning Markov Decision Processes offline RL reward shaping large language model adaptation supervised fine-tuning parameter-efficient methods LoRA QLoRA RLHF DPO GRPO Python PyTorch Ray DeepSpeed FSDP Megatron-LM

Benefits

Term Life Insurance
Medical Insurance
GrabFlex benefits package
Parental leave
Birthday leave

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Director, Reinforcement Learning & Agentic Post-Training

Blue Yonder · Posted Nov 2, 2026

Paris
Manager

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Lead the technical strategy and team for reinforcement learning and agentic post‑training of LLM‑based agents in 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.

Requirements

Nice to have: NVIDIA Nemotron
Nice to have: NVIDIA NeMo
Nice to have: Megatron
Nice to have: vLLM
Nice to have: Ray

Skills

Python PyTorch reinforcement learning

LLM post‑training

supervised fine‑tuning

preference data

reward modeling

policy optimisation

rejection sampling

policy evaluation

environment design

production constraints

NVIDIA Nemotron

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