AI/ML Engineer

Description:

About Us:

Astiva Health, Inc., located in Orange, CA is a premier health plan provider specializing in Medicare and HMO services. With a focus on delivering comprehensive care tailored to the needs of our diverse community, we prioritize accessibility, affordability, and quality in all aspects of our services. Join us in our mission to transform healthcare delivery and make a meaningful difference in the lives of our members.

SUMMARY:

We are seeking a skilled and adaptable AI/ML Engineer to join our fast-moving team building impactful AI solutions in healthcare. Our work focuses on extracting and interpreting data from unstructured medical documents, improving clinical coding accuracy, streamlining administrative processes, and enhancing patient outreach.

Projects will evolve rapidly, from fine-tuning large language models (LLMs) on specialized medical PDFs, to optimizing OCR pipelines in Azure, and new challenges emerge regularly. This role suits someone who thrives in ambiguity, enjoys hands-on model development, and wants to directly influence healthcare delivery through applied AI/ML.

ESSENTIAL DUTIES AND RESPONSIBILITIES include the following:

Design, fine-tune, and optimize large language models (LLMs) and multimodal models for healthcare-specific NLP tasks, such as information extraction, classification, and summarization from clinical documents (e.g., medical charts, patient files, scanned forms).
Develop and improve document understanding pipelines, including fine-tuning OCR / layout-aware models (especially in cloud environments like Azure AI, Azure Foundry) to handle real-world variability in medical forms, handwriting, and scanned PDFs.
Build and iterate on end-to-end ML solutions that transform unstructured healthcare data into structured, actionable insights
Collaborate closely with clinicians, product managers, data annotators, and engineers to define problems, curate/annotate datasets, evaluate model performance against clinical and business metrics, and iterate quickly.
Deploy models into production environments (cloud-based inference, batch processing, or API endpoints) with attention to latency, cost, scalability, and healthcare compliance considerations (HIPAA, data privacy).
Stay current with advancements in LLMs, vision-language models, efficient fine-tuning techniques (LoRA/QLoRA, PEFT), RAG, multimodal AI, and domain-specific healthcare AI research.
Contribute to a culture of rapid prototyping, rigorous evaluation, and continuous improvement in a dynamic project landscape where priorities can shift based on new opportunities or stakeholder needs.
Other duties as assigned

OTHER SKILLS and ABILITIES:

Hands-on experience with Azure AI services, Azure Machine Learning, OpenAI on Azure, and Microsoft Foundry
Experience with clinical NLP libraries (scispaCy, medspaCy, cTAKES)
Familiarity with RAG architectures for grounding model decisions
Experience with weak supervision or noisy-label learning
Knowledge of temporal reasoning or longitudinal modeling
Exposure to knowledge graphs or ontology-driven systems
Familiarity with healthcare vocabularies and ontologies:
ICD-10
SNOMED CT
RxNorm (or similar)
Understanding of clinical documentation structure (HPI, Assessment & Plan, medications, etc.

EXPERIENCE

Bachelor’s Degree in related field
2-4+ years of experience in software engineering, machine learning, or applied NLP
Demonstrated experience taking ML systems from prototype to production
Experience collaborating with non-technical domain experts (e.g., medical coders, clinicians)

BENEFITS:

401(k)
Dental Insurance
Health Insurance
Life Insurance
Vision Insurance
Paid Time Off
Free catered lunches
Requirements:

REQUIRED TECHNICAL SKILLS:

Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or equivalent)
Hands-on experience with NLP applied to unstructured text
Experience working with LLMs, including:
Prompting strategies
Fine-tuning for classification or extraction tasks
Model evaluation and error analysis
Experience designing or consuming annotation pipelines and labeled datasets
Familiarity with structured prediction problems (multi-label classification, ranking, or probabilistic inference)
Ability to reason about and mitigate model bias, label noise, and false positives
Strong understanding of production ML systems (versioning, monitoring, iteration)
Experience working with sensitive or regulated data (e.g., HIPAA-covered healthcare data), including privacy-aware data handling and secure ML workflows