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Grab

Senior Principal Machine Learning Engineer (Fulfilment)

Grab is hiring a Senior Principal Machine Learning Engineer (Fulfilment) in Beijing, China. Level rates it ; you can apply on Grab.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, fine tuning and AI research among its tools and skills.
  • Level classes it as a lead role.
  • Grab has 444 other open roles on Level.
  • Posted in the last few days.

AI in this role

PyTorch
fine-tuningai-research

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

The Fulfilment Tech Family builds distributed systems and machine learning platforms that power Grab's multi-sided marketplace across Southeast Asia. We process hundreds of millions of real-time requests each day to manage supply allocation, dispatch, order matching, and dynamic pricing across mobility and deliveries. Our mission is to balance customer reliability, driver-partner earnings, and operational efficiency under volatile real-world conditions. We foster a collaborative, research-to-production culture focused on scalable algorithmic impact.

Get to Know the Role

As Senior Principal Data Scientist, you will be the senior individual contributor lead architecting Grab's next generation of autonomous marketplace decisioning systems. Working at the intersection of reinforcement learning, latent world models, and generative AI, you will guide technical strategy and translate research into production systems serving millions of users. You will be the technical lead for a specialised team of scientists and engineers, reporting directly to the Head of Data Science. You will also based full-time onsite at Grab's Beijing office. If you are passionate about pioneering real-time, learning-driven marketplace systems at massive scale, join us!

The Critical Tasks You Will Perform

  • You will architect and implement end-to-end reinforcement learning pipelines (offline RL, model-based RL, and simulation) to jointly automate pricing, dispatch, and supply repositioning decisions.
  • You will design latent world models and spatio-temporal marketplace state representations that capture complex supply-demand interactions and multi-temporal levers.
  • You will establish formal off-policy evaluation (OPE) frameworks, conservative value estimation baselines, and safety guardrails to de-risk live deployment in production.
  • You will build behavioural simulation systems using fine-tuned open-source large language models (LLMs) to forecast and simulate user and partner decision dynamics.
  • You will partner with backend, streaming, and platform engineering teams to deploy low-latency, closed-loop decision agents into high-throughput production environments.
  • You will leverage modern Generative AI development tools and AI-assisted workflows to accelerate prototyping, code quality, testing, and technical documentation across the team.

What Essential Skills You Will Need

  • You have a PhD in Computer Science, Operations Research, Applied Mathematics, or a related quantitative discipline with at least 10 years of post-degree experience leading complex machine learning research and production initiatives (or Master’s degree with 12+ years of equivalent experience).
  • You are proficient in reinforcement learning foundations, including Markov Decision Processes, stochastic control, offline RL, and reward shaping for sequential multi-objective decision systems.
  • You have proven experience deploying production ML/RL systems operating under real-time constraints, non-stationary data, and distribution shifts.
  • You have hands-on expertise with large language model adaptation and alignment, including supervised fine-tuning (SFT), parameter-efficient methods (LoRA/QLoRA), and preference tuning (RLHF, DPO, or GRPO).
  • You have hands-on proficiency in Python, PyTorch, and distributed training/compute platforms such as Ray, DeepSpeed, FSDP, or Megatron-LM.
  • You have experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

  • We have your back with Term Life Insurance and comprehensive Medical Insurance.
  • With GrabFlex, create a benefits package that suits your needs and aspirations.
  • Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
  • We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
  • Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For At Grab

We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

How we rate this

Senior Principal Machine Learning Engineer (Fulfilment) at Grab rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningAI ResearchPyTorch

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$198k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Tell me about a research question you investigated. What did you find?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, AI Research, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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