Senior Machine Learning Engineer, League
Small Language Models
CA$154.6-189k
+ Bonus + Equity
AWS
GCP
Senior and Expert level
Remote from Canada
Consumer experience platform for healthcare
Be an early applicant
Consumer experience platform for healthcare
501-1000 employees
HealthcareB2BPersonal healthSaaSData Analysis
Be an early applicant
CA$154.6-189k
+ Bonus + Equity
AWS
GCP
Senior and Expert level
Remote from Canada
501-1000 employees
HealthcareB2BPersonal healthSaaSData Analysis
Company mission
To power next-generation healthcare experiences with a platform built to enable CX transformation.
Role
Who you are
- 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
- 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
What the job involves
- 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
- 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
- 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
- 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
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
Our take
Health benefits are a top requirement for anyone seeking a new job in the US today, but classic one-size-fits-all plans are no longer regarded fit for service. League's platform - for payers, providers, consumer health businesses, and employees - connects all benefits in one HIPAA-compliant place, to enable all bodies involved with health benefits to provide better customer healthcare experiences.
League is already one of the bigger players in the employee wellness space, having capitalised on the recently accepted correlation between employee health and productivity to secure huge clients like Uber, Shopify and Unilever. The company has seen significant investment, which will support the scaling of its platform-as-a-service offering. With more than 63 million contracted users, it is securing its position as the leading digital infrastructure for an integrated health ecosystem.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Top investors
-4% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Feb 2022
$95m
SERIES C
Jul 2018
$47.1m
SERIES B
Total funding: $171.1m
Company benefits
- Flexible medical and dental plans
- 401(k) and RRSP matching programs
- Health, lifestyle, learning and family spending accounts
- Employee stock option program (ESOP)
- Generous leaves (including parental and sabbatical)
- Flexible time off
Company HQ
Entertainment District, Toronto, ON
Leadership
Michael Serbinis
(Founder & CEO)Founder of DocSpace and Kobo. Experience as CEO for Three Angels Capital and Board Director for Vector Insitute, among other prominent business leadership roles.
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