Senior Machine Learning Engineer (Small Language Models)

• Translate emerging research into practical implementations

• Rapidly experiment with model architectures and optimization techniques

• Leverage modern AI tools and frameworks to accelerate development

• 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

• 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

• 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

• 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

• 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.

• 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

• 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

• 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

• 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

• 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.

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