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 implementationsRapidly experiment with model architectures and optimization techniquesLeverage modern AI tools and frameworks to accelerate developmentYou 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 qualityLeverage modern tooling and AI-assisted workflows to accelerate iteration cyclesApplied AI & Systems IntegrationBuild applied systems that connect models, data pipelines, and evaluation frameworksFocus on “wiring together” components across model training, evaluation, and deployment workflowsCollaborate with engineering teams to transition promising experiments into production environmentsContribute to training data design, including curation, labeling strategies, and synthetic data generationWork with data partners to explore AI-driven insights and improvements to model performanceDefine and run experiments to assess model performance across accuracy, reasoning, and safety dimensionsContribute to building lightweight evaluation frameworks and benchmarking approachesAI-Native Development PracticesActively leverage AI tools (e.g., Copilot, LLM-assisted coding, research copilots) to improve productivity and experimentation speedDocument and share workflows that improve how the team builds and evaluates modelsCross-Functional CollaborationPartner with Product, Platform Engineering, and AI Orchestration teams to integrate models into real-world use casesCommunicate complex technical concepts clearly to cross-functional stakeholdersAbout 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 evaluation frameworks — not just build models, but know whether they’re working.Strong technical communication skills; able to present complex model decisions clearly to cross‑functional and executive audiences.Bachelor’s or graduate degree in Computer Science, Machine Learning, or equivalent experience.AI Fluency & Ways of WorkingUse AI tools as part of your daily workflowto enhance productivity, problem‑solving, and decision‑making (e.g., drafting, analysis, coding, research, or process automation)Apply judgment and accountabilitywhen using AI by reviewing outputs for accuracy, bias, and quality before useContinuously learn and adaptas new AI tools and capabilities emerge, incorporating them into your ways of workingIdentify opportunities to improve how work gets donefrom personal productivity to team‑level workflows by leveraging AI effectivelyOperate with strong data responsibility and security awareness , especially when working with sensitive or regulated informationHow this scales by level:Individual Contributors : Use AI to improve personal productivity and quality of outputSenior ICs / Managers : Integrate AI into team workflows and improve processesLeaders : Drive AI adoption at the organizational level and shape how work is done across teamsWhat we look for:Demonstrated experience using AI tools in a practical, responsible wayCuriosity and openness to experimenting with new technologiesAbility to balance efficiency with quality and sound judgmentSecurity-Related ResponsibilitiesCompliance with Information Security PoliciesCompliance with League’s secure coding practiceResponsibility and accountability for executing League's policies and proceduresNotification of HR, Legal, Compliance & Security of any incidents, breaches or policy violationsWork Location We have a mix of office‑centric roles based in our vibrant Toronto office, and remote‑eligible roles based anywhere in Canada or US. Each job posting will indicate where the role will be based. Regardless of the role’s posted location, all Toronto‑area Leaguers (living within 65 km of our downtown HQ) collaborate in‑office Monday through Thursday. Depending on your distance to the office, you’ll enjoy 10 or 20 Flexible Remote Days each quarter for focus and deep‑work time. We are committed to fostering a meaningful work environment and connections for all Leaguers regardless of location.We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. If you are an individual in need of assistance at any time during our recruitment process, please contact us at #J-18808-Ljbffr