Senior Machine Learning Engineer (Small Language Models)

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.

• 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

Originally posted on Himalayas