Applied AI Scientist

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: Model Development & Experimentation 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 Data & Training Strategy 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 Evaluation & Model Quality 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 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 Working At League, we are an AI-native organization . We expect all employees regardless of role or level to thoughtfully leverage AI to improve the quality, speed, and impact of their work. What this means in practice: Use AI tools as part of your daily workflow to enhance productivity, problem-solving, and decision-making (e.g., drafting, analysis, coding, research, or process automation) Apply judgment and accountability when using AI by reviewing outputs for accuracy, bias, and quality before use Continuously learn and adapt as new AI tools and capabilities emerge, incorporating them into your ways of working Identify opportunities to improve how work gets done from personal productivity to team-level workflows by leveraging AI effectively Operate with strong data responsibility and security awareness , especially when working with sensitive or regulated information How this scales by level: Individual Contributors: Use AI to improve personal productivity and quality of output Senior ICs / Managers: Integrate AI into team workflows and improve processes Leaders: Drive AI adoption at the organizational level and shape how work is done across teams What we look for: Demonstrated experience using AI tools in a practical, responsible way Curiosity and openness to experimenting with new technologies Ability to balance efficiency with quality and sound judgment Security-Related Responsibilities Compliance with Information Security Policies Compliance with League’s secure coding practice Responsibility and accountability for executing League's policies and procedures Notification of HR, Legal, Compliance & Security of any incidents, breaches or policy violations CANADA APPLICANTS ONLY: The Canada-specific compensation range below for this full-time position is exclusive of bonus, equity and benefits. This range reflects the minimum and maximum target for base salaries for the position across all Canadian locations. The salary range is intentional to account for the performance and career progressions a Leaguer will experience in the role throughout their time at League. Where in the band you may land is determined by job-related skills/experience. Your recruiter can share more about the specific salary range specific to your skills and experience during the hiring process. Compensation range for Canada applicants only $154,600 — $189,000 CAD Our employees come from different backgrounds, and we celebrate those differences. We are looking for the best candidates for our open roles, but do not expect applicants to meet every qualification in order to be considered. If you are excited about what you could accomplish at League and believe you can add value to our team, we would love to hear from you. 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 [email protected] . Our Application Process: Applying to a role you love can be exhausting, and understanding the next steps can feel vague and uncertain. You have done the hard part of submitting your application; let's do ours by sharing potential next steps You should receive a confirmation email after submitting your application. A recruiter (not a computer) reviews all applications at League. If we see alignment with League's needs, a recruiter will reach out to learn more about your goals. The recruiter will also share the team-specific interview process depending on the roles you are exploring. The final step is an offer, which we hope you will accept! Prior to joining us, we conduct reference and background checks. Additional checks could be required for US Candidates, depending on the role you are exploring. Here are some additional resources to learn more about League: Learn about our platform, leadership team and partners Highmark Health, Google Cloud, League: new digital front door to seamless care Former Providence President and Workday EVP of Corporate Strategy join League Board of Directors League raises $95 million USD in Series C to build world’s leading healthcare CX platform Forbes x League: The Platformization Of Healthcare Is Here Fast Company x League: If we want better innovations in healthtech, we need more competition Work 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. Recognize and Avoid Employment scams. Practice safe job searching. Scammers are getting craftier and leveraging fake job postings to get personal information. Know the warning signs and protect yourself from scammers. Learn more here . Use of AI Notice We are committed to ensuring fairness and transparency throughout our hiring process. League may use Artificial Intelligence (AI) tools to assist in the screening of applicants for this position. Please check out our stance on using AI in recruitment here . 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