Senior Machine Learning Engineer (Small Language Models)

League is a leading healthcare consumer experience platform utilizing AI, serving over 63 million people worldwide, and partnering with major healthcare brands. They focus on delivering personalized healthcare solutions and are backed by significant venture capital funding.

Location: Remote (US and Canada)

Salary: $154,600—$189,000 CAD

Responsibilities

• Design and implement experiments for fine-tuning, distillation, and optimization of small language models (1–10B parameters)
• Prototype and evaluate new model performance, efficiency, and reasoning quality
• Build systems connecting models, data pipelines, and evaluation frameworks
• Contribute to training data design, including curation, labeling, and synthetic data generation
• Define and run experiments to assess model performance across accuracy, reasoning, and safety
• Leverage AI tools to improve productivity and experimentation speed
• Partner with cross-functional teams to integrate models into real-world use cases
• Communicate complex technical concepts clearly to stakeholders

Requirements

• 5+ years of hands-on experience in applied ML/AI engineering, focusing on language model development, fine-tuning, or NLP systems
• Proven track record shipping fine-tuned or distilled LLMs/SLMs (1–10B parameters) to production
• Deep expertise in PEFT techniques — LoRA, QLoRA, adapter tuning — and model quantization and distillation pipelines
• Hands-on experience with RLHF/RLAIF, reward modeling, or safety alignment workflows
• Strong background in data curation, labeling pipeline design, and synthetic data generation
• Proficiency with model training frameworks and tooling: NeMo, Hugging Face Transformers, Axolotl, or equivalent
• Experience with model serving stacks: vLLM, Triton, or similar; inference optimization techniques
• Comfort operating on cloud infrastructure (GCP, Vertex AI, AWS) and GPU resource management
• Solid understanding of healthcare data privacy and safety requirements: HIPAA, FHIR, clinical ontologies
• Ability to define and own evaluation frameworks and assess model performance
• Strong technical communication skills; ability to present complex model decisions clearly
• Bachelor's or graduate degree in Computer Science, Machine Learning, or equivalent

Additional Information

• AI-native organization encouraging the use of AI tools in daily workflows
• Focus on responsible AI use, data security, and continuous learning
• Flexible work arrangements with in-office and remote options in US and Canada