Senior Machine Learning Engineer (Small Language Models)

About League

Founded in 2014, League is the leading healthcare consumer experience (CX) platform, powered by artificial intelligence (AI), reaching more than 63 million people around the world and delivering the highest level of personalization in the industry. Payers, providers, and consumer health partners build on League’s platform to deliver high-engagement healthcare solutions proven to improve health outcomes. League has raised over $285 million in venture capital funding to date, powering the digital experiences for some of healthcare’s most trusted brands, including Highmark Health, Manulife, Medibank, and Shoppers Drug Mart.

Position Summary

League is seeking a Senior ML Engineer to join our AI Models team, focused on advancing innovation in small language models (SLMs) and applied AI systems.

This role sits at the intersection of research and engineering, with a strong emphasis on experimentation, model development, and applied system design. You will work closely with AI leadership to explore, prototype, and operationalize new approaches to domain-specific language models that power League’s healthcare platform.

Unlike a traditional engineering role, this position is R&D-focused, designed for someone who can:

• Translate emerging research into practical implementations
• Rapidly experiment with model architectures and optimization techniques
• Leverage modern AI tools and frameworks to accelerate development

You will contribute to building League’s next generation of AI capabilities, while partnering with platform and product teams to bring high-impact innovations into production.

In this role, you will:

• Design and implement experiments across fine-tuning, distillation, and optimization of small language models (1–10B parameters)
• Rapidly prototype and evaluate new approaches to model performance, efficiency, and reasoning quality
• Leverage modern tooling and AI-assisted workflows to accelerate iteration cycles

Applied AI & Systems Integration

• Build applied systems that connect models, data pipelines, and evaluation frameworks
• Focus on “wiring together” components across model training, evaluation, and deployment workflows
• Collaborate with engineering teams to transition promising experiments into production environments
• Contribute to training data design, including curation, labeling strategies, and synthetic data generation
• Work with data partners to explore AI-driven insights and improvements to model performance
• Define and run experiments to assess model performance across accuracy, reasoning, and safety dimensions
• Contribute to building lightweight evaluation frameworks and benchmarking approaches

AI-Native Development Practices

• Actively leverage AI tools (e.g., Copilot, LLM-assisted coding, research copilots) to improve productivity and experimentation speed
• Document and share workflows that improve how the team builds and evaluates models

Cross-Functional Collaboration

• Partner with Product, Platform Engineering, and AI Orchestration teams to integrate models into real-world use cases
• Communicate complex technical concepts clearly to cross-functional stakeholders

About you:

• 5+ years of hands‑on experience in applied ML/AI engineering, with a focus 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; familiarity with inference optimization techniques.
• Comfort operating on cloud infrastructure (GCP, Vertex AI, AWS) and with GPU resource management.
• Solid understanding of healthcare data privacy and safety requirements: HIPAA, FHIR, clinical ontologies.
• Demonstrated ability to define and own…