LLM Algorithm Engineer

Manus AI; Singapore; Presencial; SGD 120 000 - 200 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity & Shared success

Unlimited Manus Tokens

Resumo da oferta

Manus AI is seeking a researcher to advance LLM post-training methods and experiments in Singapore. You will work across data strategies, training recipes, and evaluation to diagnose performance gaps and translate insights into product improvements.

You will refine data usage, design robust benchmarks, and analyze model behavior to drive practical outcomes. Collaboration with product and engineering is essential to deliver real-world gains.

Qualificações

• Solid foundations in machine learning and LLMs, with research or practical experience in foundation model training or post-training.
• Proficiency in setting up experiments independently and analyzing results.
• Ability to plan improvements based on evaluations and use cases.
• Active engagement with relevant research and ability to formulate testable hypotheses.

Responsabilidades

• Research and refine LLM post-training methods and training recipes to improve performance.
• Build and iterate on training data, adjusting quality and usage strategies.
• Design benchmarks and evaluations for target capabilities and practical use cases.
• Analyze model behavior, form hypotheses, and test them through training experiments.
• Collaborate with product, engineering and model teams to apply feedback and assess results.

Conhecimentos

ML foundations

LLM training experience

Experiment design

Evaluation and analysis

Ferramentas

PyTorch

TensorFlow

Descrição da oferta de emprego

Improve LLM and agent capabilities through post-training research and experimentation. Work across data strategies, training methods and evaluation to diagnose task performance issues and bring research improvements into products.

Responsibilities

• Research and refine LLM post-training methods and training recipes, designing experiments around target tasks to improve performance and generalization.
• Build and iterate on training data, adjusting quality, distribution and usage strategies in response to capability goals and experimental results.
• Design benchmarks and evaluations that cover target capabilities and practical use cases, providing a consistent basis for comparing training approaches.
• Analyze model behavior and recurring failures, formulate improvement hypotheses, and test them through training experiments while documenting results and their conditions.
• Work with product, engineering and model partner teams to incorporate application feedback into data, training and evaluation, and assess results on real tasks.

Requirements

• Solid foundations in machine learning and LLMs, with research or practical experience in large-scale foundation model training or post-training and familiarity with SFT, reinforcement learning or preference optimization.
• Proficiency in mainstream deep learning frameworks, with the ability to set up experiments independently, analyze training results and diagnose problems.
• Understanding of how data and training methods affect model performance, with the ability to plan improvements based on evaluations and practical use cases.
• Active engagement with relevant research and independent research judgment, with the ability to formulate testable hypotheses from open-ended problems and evaluate them rigorously.

What we offer:

• Build at the Frontier of AI Agents - Work with a fast-paced team to turn frontier AI breakthroughs into real-world impact.
• Equity & Shared success - Our share incentive plan enables employees to participate in Manus’s long-term growth and share in the value we create together.
• Unlimited Manus Tokens - Enjoy unlimited Manus tokens to experiment, build, and supercharge your productivity.

If you're passionate about cutting-edge technology and making a real impact, we’d love to hear from you!