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 solid 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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📌 Senior LLM Research Engineer (Toronto)
🏢 Encore Technical Solutions
📍 Toronto