Senior Machine Learning Engineer (Small Language Models)

TL;DR

Senior Machine Learning Engineer (Small Language Models): Design, fine-tune, and optimize small language models (1–10B parameters) for healthcare applications with an accent on experimentation, efficiency, and domain-specific adaptations. Focus on prototyping advanced architectures, integrating with data pipelines, and deploying production-ready AI systems.

Location: Canada - Remote. CANADA APPLICANTS ONLY. Remote-eligible roles based anywhere in Canada.

Salary: $154,600–$189,000 CAD

Company

Leading healthcare consumer experience (CX) platform powered by AI, reaching 63 million people worldwide and partnering with major health brands.

What you will do

• Design and implement experiments for fine-tuning, distillation, and optimization of small language models.
• Prototype and evaluate model performance, efficiency, and reasoning using modern AI tools.
• Build applied systems connecting models, data pipelines, evaluation frameworks, and production workflows.
• Contribute to training data design, curation, labeling, and synthetic data generation.
• Define evaluation frameworks for accuracy, reasoning, and safety; benchmark model quality.
• Collaborate with product, platform, and AI teams to integrate models into real-world use cases.

Requirements

• 5+ years in applied ML/AI engineering focused on language models, fine-tuning, or NLP.
• Proven shipping of fine-tuned/distilled LLMs/SLMs (1–10B parameters) to production.
• Deep expertise in PEFT (LoRA, QLoRA, adapters), quantization, distillation.
• Experience with RLHF/RLAIF, reward modeling, safety alignment.
• Strong data curation, labeling pipelines, synthetic data generation.
• Proficiency in NeMo, Hugging Face Transformers, Axolotl; model serving (vLLM, Triton).
• Cloud infrastructure (GCP, Vertex AI, AWS), GPU management.
• Understanding of healthcare privacy: HIPAA, FHIR.
• Bachelor's or graduate degree in CS, ML, or equivalent.

Culture & Benefits

• AI-native organization: Leverage AI tools daily for productivity, with judgment and accountability.
• Remote-eligible in Canada; Toronto-area employees collaborate in-office Mon-Thu with flexible remote days.
• Focus on continuous learning, AI adoption, data security, and cross-functional collaboration.
• Equity, bonus, benefits included in compensation.

Hiring process

• Application review by recruiter (human-reviewed).
• Recruiter outreach for goals and team-specific interview process.
• Reference and background checks prior to offer.