Senior Machine Learning Engineer (Small Language Models)

League Inc.

23h•Hybrid

About The Position

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.

Requirements

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.

Responsibilities

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