Machine Learning Engineer - LLM / RL

Insilico Medicine; Montreal; On-site; CAD 90,000 - 130,000; Full time

Job summary

Une entreprise de biotechnologie de premier plan recherche un ingénieur en apprentissage automatique à Montréal pour développer des modèles GenAI destinés aux tâches moléculaires. Ce rôle implique la conception de workflows RL, l'optimisation de pipelines et la collaboration avec des leaders de recherche. Les candidats doivent avoir une solide formation en informatique ou en chimie, ainsi qu'une expérience avérée en ingénierie ML.

Qualifications

• Au moins 5 ans d'expérience en ML engineering.
• Expertise dans le déploiement de modèles de transformeurs.
• Expérience pratique avec des modèles génératifs.

Responsibilities

• Concevoir et optimiser des workflows de fine-tuning RL pour LLMs.
• Construire des pipelines d'entraînement haute performance.
• Adapter des modèles pour des tâches de découverte de médicaments.

Skills

ML Engineering

Transformers

RL Algorithms

Reward Integration

Tooling & Collaboration

Drug Discovery & Molecular Design

Generative Models

Education

M.S., Ph.D. in Computer Science, Machine Learning, or related discipline

Tools

PyTorch

Hugging Face Transformers

Docker

Sagemaker

Job description

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

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.
• 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.

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