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Encore Technical Solutions Inc.

Senior LLM Research Engineer

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 4, 2026
Posting language: English
Posted: Oct 5, 2026

Original job description

Senior LLM Research Engineer

Full-Time Permanent

Enterprise Client - Downtown Toronto

Hybrid/Remote

We're looking for a Senior LLM Research Engineer to help build and optimize advanced language models for complex, real-world applications. This role spans the full lifecycle of model development, from dataset creation and post-training through inference optimization, evaluation, and continuous improvement.

You'll work on challenging domains where high-quality training data and ground truth are often unavailable, requiring creative approaches to data generation, labeling, evaluation, and model alignment. Success in this role comes from combining strong research capabilities with practical engineering execution.

What You'll Do

Lead post-training workflows, including SFT, RL-based training, distillation, and model alignment
Design and implement robust evaluation frameworks, benchmarks, and regression testing suites
Build high-quality training and evaluation datasets from large, unstructured corpora, including synthetic data generation
Train and optimize models on distributed GPU infrastructure, troubleshooting performance and scalability challenges
Improve inference efficiency through quantization, parallelization, decoding optimizations, and serving performance tuning
Analyze results and translate experiments into data-driven model improvement decisions

What You'll Bring

Strong Python expertise and experience with modern LLM frameworks such as PyTorch, Hugging Face Transformers, TRL, DeepSpeed, vLLM, or similar tools
Hands-on experience fine-tuning and aligning large language models using supervised and reinforcement learning approaches
Deep understanding of distributed training, model scaling, and GPU optimization
A rigorous approach to evaluation, experimentation, and measurement
Ability to bridge research and engineering, delivering production-ready solutions with strong software development practices

Preferred Experience

Creating labeled datasets from unstructured data, including synthetic data generation at scale
Designing LLM evaluation methodologies, rubrics, and LLM-as-judge systems
Inference optimization, including quantization, speculative decoding, and performance tuning
Building AI solutions for complex, domain-specific tasks where answers require nuance, reasoning, and judgment

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More about this job

Responsibilities

Lead post-training workflows, including supervised fine-tuning, reinforcement learning, distillation, and model alignment, while building datasets and evaluation frameworks. Train and optimize models on distributed GPU infrastructure, improve inference performance, and use experimental results to guide model improvements.

Requirements

Requires strong Python expertise and hands-on experience fine-tuning and aligning large language models with supervised and reinforcement learning methods. Candidates should understand distributed training and GPU optimization, apply rigorous evaluation practices, and be able to deliver production-ready solutions using modern LLM frameworks.

Skills

Python
Large Language Models
PyTorch
Hugging Face Transformers
Supervised Fine-Tuning
Reinforcement Learning
Model Alignment
Distributed Training
GPU Optimization
Dataset Creation
Synthetic Data Generation
Model Evaluation
Inference Optimization
Quantization
DeepSpeed
vLLM

Visa sponsorship

Not detected in the job text

Keywords

Python
Large Language Models
PyTorch
Hugging Face Transformers
TRL
DeepSpeed
vLLM
Supervised Fine-Tuning
Reinforcement Learning
Distillation
Model Alignment
Distributed Training
GPU Infrastructure
GPU Optimization
Synthetic Data Generation
Dataset Labeling
Evaluation Frameworks
Benchmarks
Regression Testing
Inference Optimization
Quantization
Speculative Decoding
Parallelization
LLM-as-Judge
Model Scaling
Data-Driven Experimentation
Production Engineering

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