Senior Machine Learning Engineer, Small Language Models

🕒 April 16

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League

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2014

⚕️ Healthcare Insurance

☁️ SaaS

🤖 Artificial Intelligence

Healthcare Insurance • SaaS • Artificial Intelligence

League is a leading healthcare technology company that provides a platform designed to transform the healthcare consumer experience. Its platform offers digital infrastructure for data-driven, personalized consumer interactions, enhancing engagement and satisfaction for health plans, payers, and providers. League's solutions focus on creating innovative member experiences, acting as a digital destination for consumer health, and providing omni-channel patient experiences. With a focus on data security and compliance, the platform supports personalized care and modernizes healthcare interactions using data and AI. Additionally, League collaborates with partners to enrich the healthcare ecosystem and facilitates technology teams in developing their own solutions through its developer program.

📋 Description

• 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

🎯 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.

🏖️ Benefits

• Health insurance • Flexible remote days • Professional development opportunities