AI Scientist Senior II

Cambia Health Solutions•Portland, OR

4d•Hybrid

About The Position

This is a hybrid role (3 days/week in office) at our Burlington, Renton, Spokane, Vancouver, Portland, Medford, Salt Lake City, Boise, Lewiston, or Fargo offices. Candidates must reside within commutable distance of that location or be willing to relocate. The Applied AI Team is focused on creating a person-focused and economically sustainable health care system. AI Scientists work with various stakeholders to design, develop, and implement data-driven solutions. This position applies deep expertise in advanced analytical tools such as generative AI, machine learning, deep learning, optimization, and statistical modeling to solve complex, high-impact business problems in the healthcare payer domain. As a Senior II AI Scientist, you will serve as a technical leader and strategic advisor, driving innovation across multiple business areas such as clinical care delivery, customer experience, and payment integrity. This is a hands-on technical leadership role – you will personally architect and build sophisticated AI solutions while simultaneously mentoring junior team members and influencing the technical direction of our AI initiatives. You will lead by example, writing production-quality code, conducting rigorous experiments, and demonstrating best practices in every aspect of AI development. This role requires not only mastery of generative AI, machine learning, and deep learning, but also strong architectural thinking, advanced software engineering capabilities, and the ability to translate ambiguous business challenges into innovative AI solutions. You will be expected to remain deeply technical, actively contributing code, developing models, and solving complex technical problems alongside your team. Your leadership will come through the quality of your technical work, your ability to tackle the most challenging problems, and your commitment to elevating the skills and capabilities of those around you. AI Scientists work closely with AI team members in the Product and Engineering tracks to collaboratively develop and deliver models and data-driven products. At the Senior II level, you will lead cross-functional initiatives, establish best practices through your own exemplary work, and serve as a subject matter expert to both technical and business stakeholders – all in service of making our members' health journeys easier. If you're an accomplished AI Scientist with a proven track record of delivering impactful solutions through hands-on technical excellence and leading others through example in the healthcare industry, apply for this exciting opportunity today!

Requirements

Degree (masters or PhD preferred) in a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically at least 12 years of related work experience. Equivalent combination of education and experience will be considered.
Recognized expert in generative AI, machine learning, and data science with ability to architect complex, novel solutions and define technical vision aligned with business strategy.
Deep understanding of the healthcare industry (preferred) with ability to identify and prioritize high-value AI opportunities, evaluate emerging technologies, and lead multiple complex projects from conception to production.
Mastery of advanced AI/ML techniques with ability to innovate beyond existing patterns, combined with expert-level Python programming and strong software engineering principles (design patterns, testing, CI/CD).
Deep expertise in working with complex, real-world data challenges (noisy, high-dimensional, sparse, imbalanced, biased) across multiple data domains (e.g., claims, clinical, member engagement).
Deep expertise in multiple AI modeling techniques with ability to select and combine methods innovatively, design scalable architectures for offline and online systems, and implement MLOps, model governance, and responsible AI practices.
Advanced SQL and data engineering skills, including optimization of complex queries and data pipeline design.
Ability to tackle ambiguous, ill-defined problems and structure them into actionable AI initiatives that create measurable business value.
Proactive identification of AI opportunities for strategic advantage, with ability to anticipate technical risks, design mitigation strategies, and conduct research and experimentation including A/B testing and causal inference.
Proven ability to mentor and develop junior AI Scientists while establishing and evangelizing best practices, coding standards, and technical processes.
Strong leadership presence with ability to influence technical decisions across the organization, lead cross-functional teams, manage stakeholder relationships, and build productive partnerships across departments.
Excellent communication skills with ability to present complex technical concepts to audiences ranging from technical teams to C-level executives.
Strong ability to translate business strategy into AI opportunities and technical requirements, quantify business impact and ROI of AI initiatives, and balance technical excellence with pragmatic business delivery.
Understanding of healthcare payer operations, regulations, and industry trends.
Deep understanding of transformer architectures, attention mechanisms, scaling laws, and experience with multiple model families (GPT, BERT, etc.).
Expertise in parameter-efficient fine-tuning (LoRA, QLoRA, Adapters), instruction tuning, domain adaptation, and advanced prompting techniques (chain-of-thought, tree-of-thought, meta-prompting).
Advanced RAG architectures, hybrid search strategies, knowledge base optimization, and experience designing AI agent systems with tool use, planning, and multi-agent collaboration.
Deep expertise in evaluation methodologies (automated metrics, LLM-as-judge, human evaluation), alignment techniques (RLHF, DPO, constitutional AI), inference optimization, caching strategies, and cost management.
Experience with vision-language models and multimodal understanding, plus deep understanding of bias detection and mitigation, hallucination reduction, safety considerations, and privacy-preserving techniques.
Expert-level proficiency with Hugging Face ecosystem, LangChain, LlamaIndex, vector databases, and emerging GenAI tools.
Deep expertise across supervised, unsupervised, semi-supervised, and reinforcement learning paradigms, including ensemble methods (boosting, bagging, stacking), time series forecasting, and causal inference.
Deep understanding of optimization algorithms, convergence properties, custom loss function design, experimental design, statistical testing, and bias-variance tradeoff analysis.
Experience with automated model selection, hyperparameter optimization at scale, and advanced techniques for knowledge transfer and few-shot learning.
Deep understanding of CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, and diffusion models, plus advanced optimization methods, learning rate scheduling, and convergence analysis.
Advanced techniques including dropout variants, batch normalization, layer normalization, and architectural regularization, with expertise in NLP, computer vision, or speech processing.
Knowledge of quantization, pruning, distillation, and efficient inference techniques.
Advanced linear algebra (matrix decompositions, eigen analysis, numerical methods), probability and statistics (Bayesian methods, hypothesis testing, experimental design), optimization theory (convex optimization, constrained optimization, stochastic optimization), and information theory.
Understanding of data warehousing, data lakes, modern data stack components, plus advanced SQL including query optimization, window functions, CTEs, and performance tuning.
Design patterns, testing strategies (unit, integration, end-to-end), version control, CI/CD, model versioning, experiment tracking, model monitoring, and deployment strategies.
Experience with distributed training, data parallelism, scalable data processing (Spark, Dask, Ray), and proficiency with cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI).

Nice To Haves

Deep understanding of the healthcare industry
Deep understanding of transformer architectures, attention mechanisms, scaling laws, and experience with multiple model families (GPT, BERT, etc.)
Expertise in parameter-efficient fine-tuning (LoRA, QLoRA, Adapters), instruction tuning, domain adaptation, and advanced prompting techniques (chain-of-thought, tree-of-thought, meta-prompting)
Advanced RAG architectures, hybrid search strategies, knowledge base optimization, and experience designing AI agent systems with tool use, planning, and multi-agent collaboration
Deep expertise in evaluation methodologies (automated metrics, LLM-as-judge, human evaluation), alignment techniques (RLHF, DPO, constitutional AI), inference optimization, caching strategies, and cost management
Experience with vision-language models and multimodal understanding, plus deep understanding of bias detection and mitigation, hallucination reduction, safety considerations, and privacy-preserving techniques
Expert-level proficiency with Hugging Face ecosystem, LangChain, LlamaIndex, vector databases, and emerging GenAI tools
Deep expertise across supervised, unsupervised, semi-supervised, and reinforcement learning paradigms, including ensemble methods (boosting, bagging, stacking), time series forecasting, and causal inference
Deep understanding of optimization algorithms, convergence properties, custom loss function design, experimental design, statistical testing, and bias-variance tradeoff analysis
Experience with automated model selection, hyperparameter optimization at scale, and advanced techniques for knowledge transfer and few-shot learning
Deep understanding of CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, and diffusion models, plus advanced optimization methods, learning rate scheduling, and convergence analysis
Advanced techniques including dropout variants, batch normalization, layer normalization, and architectural regularization, with expertise in NLP, computer vision, or speech processing
Knowledge of quantization, pruning, distillation, and efficient inference techniques
Advanced linear algebra (matrix decompositions, eigen analysis, numerical methods), probability and statistics (Bayesian methods, hypothesis testing, experimental design), optimization theory (convex optimization, constrained optimization, stochastic optimization), and information theory
Understanding of data warehousing, data lakes, modern data stack components, plus advanced SQL including query optimization, window functions, CTEs, and performance tuning
Design patterns, testing strategies (unit, integration, end-to-end), version control, CI/CD, model versioning, experiment tracking, model monitoring, and deployment strategies
Experience with distributed training, data parallelism, scalable data processing (Spark, Dask, Ray), and proficiency with cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI)

Responsibilities

Lead the design and architecture of complex, multi-component AI systems that solve strategic business problems, while defining technical standards, best practices, and design patterns for AI development across the team.
Evaluate and recommend new AI technologies, frameworks, and methodologies for adoption, serving as the technical authority on AI/ML topics.
Drive innovation by researching and prototyping cutting-edge AI techniques applicable to healthcare challenges, and lead technical design reviews to ensure high-quality solutions.
Research, design, and implement novel AI solutions using state-of-the-art generative AI, machine learning, and deep learning techniques to handle complex, real-world healthcare data challenges.
Design custom algorithms and modeling approaches when existing solutions are insufficient, and develop advanced evaluation frameworks that capture business value and model behavior.
Create reusable components, libraries, and frameworks that accelerate AI development, and lead the development of production grade AI systems with robust monitoring, governance, and maintenance strategies.
Partner with business leaders to identify high-impact AI opportunities and translate ambiguous business challenges into well-defined AI problems with clear success criteria.
Design comprehensive experimentation strategies including A/B testing, causal inference, and statistical validation.
Proactively identify risks, biases, and ethical considerations in AI solutions and develop mitigation strategies, while quantifying and communicating business impact and ROI to executive stakeholders.
Design and optimize complex data pipelines for model training, evaluation, and serving, while developing advanced feature engineering strategies that unlock model performance.
Build scalable, maintainable AI systems using modern MLOps practices and cloud infrastructure, with comprehensive monitoring and observability for production systems.
Ensure data quality, governance, and compliance with healthcare regulations (HIPAA, etc.).
Mentor junior and mid-level AI Scientists, providing technical guidance and career development support through code reviews and constructive feedback.
Lead knowledge-sharing sessions, workshops, and technical presentations, while contributing to hiring and onboarding processes.
Foster a culture of continuous learning, experimentation, and technical excellence.
Lead cross-functional initiatives involving Product, Engineering, and Business stakeholders, communicating complex technical concepts effectively to both technical and non-technical audiences, including executives.
Build strong partnerships across the organization to identify opportunities and remove blockers, represent the AI team in strategic planning and roadmap discussions, and contribute to thought leadership through presentations, publications, or industry engagement.
Champion responsible AI practices including fairness, transparency, and accountability, while developing frameworks for bias detection, mitigation, and ongoing monitoring.
Ensure AI solutions comply with regulatory requirements and ethical guidelines, and lead efforts to document model decisions, assumptions, and limitations for governance purposes.

Benefits

Medical, dental and vision coverage for employees and their eligible family members, including mental health benefits.
Annual employer contribution to a health savings account.
Generous paid time off varying by role and tenure in addition to 10 company-paid holidays.
Market-leading retirement plan including a company match on employee 401(k) contributions, with a potential discretionary contribution based on company performance (no vesting period).
Up to 12 weeks of paid parental time off (eligibility requires 12 months of continuous service with Cambia immediately preceding leave).
Award-winning wellness programs that reward you for participation.
Employee Assistance Fund for those in need.
Commute and parking benefits.
Competitive base pay
Market-leading 401(k) with a significant company match
Bonus opportunities