Machine Learning Engineer - LLM/RL

Insilico Medicine; Abu Dhabi; On-site; USD 80,000 - 120,000; Full time

Job summary

An innovative firm is seeking a Machine Learning Engineer to drive advancements in GenAI models for molecular tasks. In this pivotal role, you will design and scale reinforcement learning workflows, optimize transformer training pipelines, and adapt models for drug discovery. This position offers a dynamic work environment where your contributions will significantly impact the future of pharmaceutical development. If you have a strong background in machine learning and a passion for cutting-edge technology, this is the perfect opportunity to make a difference in the industry.

Qualifications

• 5+ years of experience in ML engineering with a focus on large models.
• Expertise in training and deploying transformer models using PyTorch.

Responsibilities

• Design and scale reinforcement learning workflows for LLMs.
• Build and optimize high-throughput transformer training pipelines.

Skills

ML Engineering

Transformers

RL Algorithms

Scalable Training Pipelines

Reward Integration

Python

Education

M.S. or Ph.D. in Computer Science

Relevant experience in Machine Learning

Tools

PyTorch

Hugging Face Transformers

Docker

Sagemaker

Job description

About Insilico

Insilico Medicine is an end-to-end, AI-driven pharma/biotechnology company with a mission to accelerate drug discovery and development by leveraging our rapidly evolving, proprietary platform and models across biology, chemistry, and clinical development.

For more info, visit our website https://insilico.com

About Role

Insilico Medicine seeks a Machine Learning Engineer (LLM/RL) to build and scale GenAI models for molecular tasks.

Place of work

Office based - Abu Dhabi (Level 6, Unit 08, Block A, IRENA HQ Building Masdar City, Abu Dhabi, United Arab Emirates

Reports to

ML team leader

Responsibilities

• Design and scale reinforcement learning fine-tuning workflows for LLMs, including RLVR/RLHF approaches.
• Build and optimize high-throughput transformer training pipelines across multi-GPU and multi-node environments.
• Adapt models to domain-specific drug discovery and molecular design tasks, such as molecular generation and property prediction.
• Lead experimental design, monitor core metrics, and communicate insights and findings through presentations and/or publications.

Education

• M.S., Ph.D., or equivalent relevant experience in Computer Science, Machine Learning, Computational Chemistry, or a related quantitative discipline.

Required Skills & Experience

• ML Engineering: At least 5 years of experience in ML engineering
• Transformers: Expertise in training and deploying large transformer models using PyTorch.
• RL Algorithms: Hands-on experience with PPO/DPO/RLOO/GRPO and other related policy gradient and preference-based optimization methods.
• Scalable Training Pipelines: Proficiency in data, model, context, and pipeline parallelism.
• Reward Integration: Practical experience developing diverse reward functions and reward models.
• Tooling & Collaboration: Excellent Python skills are necessary, along with experience using Hugging Face Transformers, TRL/RLlib/verl, DeepSpeed, vLLM, Docker, Sagemaker, and CI/CD workflows.

Desirable Skills

• Senior Level: At least two years in a senior position.
• Drug Discovery & Molecular Design: Background in chemistry, cheminformatics, structural biology, or experience with molecular generative models.
• Generative Models: Knowledge of diffusion, flow-based, and other generative models.
• Research Leadership: Track record of machine learning publications.