Artificial Intelligence Research Scientist II Location: US, WA Job Description: AI Research Scientist II – Office of the CTO The mission of the Allen Institute is to unlock the complexities of bioscience and advance our knowledge to improve human health. Using an open science, multi-scale, team-oriented approach, the Allen Institute focuses on accelerating foundational research, developing standards and models, and cultivating new ideas to make a broad, transformational impact on science. The mission of the Office of the Chief Technology Officer (CTO) is not only to provide state of the art engineering infrastructure to the Allen Institute as a whole, but to also address high-risk, high-reward questions in biology through AI. We are seeking an independent and motivated Scientist in the Office of the CTO to build large AI/ML models for Biology. Reporting to the Senior Director of AI Applications, you will help develop and apply SOTA AI/ML methods for the representation, analysis and interpretation of multimodal data to understand biological systems in health and disease. This role works collaboratively across all Allen Institute teams including Brain Science, Cell Science, Immunology, Neural Dynamics, and the Seattle Hub for Synthetic Biology (SeaHub). AI/ML focus areas could include multimodal foundation models, diffusion models, causal representation learning, and beyond. At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly. We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment. We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions. Essential Functions - Develop and deploy large scale ML models that deepen our understanding of complex biological systems in health and disease by incorporating diverse biological datatypes, such as multi-omics data, microscopy data, in vivo and behavioral imaging data, electrophysiological data, and/or clinical data. Using ML methods to tokenize and embed biological data for ML models and subsequently analyze ML embeddings for benchmarking and other biological tasks - Help to establish community standards for scalability in developing, disseminating, and evaluating AI/ML/computational methods for scientific problems across the model lifecycle by testing and documenting best practices and sharing with the community - Collaborate with teams of scientists, computational biologists, and software engineers within the Allen Institute and external partners to help drive large scale AI/ML models, from best code practices to scientific impact - Collaborate with software engineers to build state-of-the-art engineering infrastructure at the Allen Institute to support large scale AI/ML research and applications, such as methods for caching and processing petabyte scale data across multiple networked GPU nodes on cloud, this person will build a research grade large scale ML infrastructure that will be hardened by SWE - Participate in institute-wide initiatives, workshops, and seminars to promote cross-disciplinary collaboration and knowledge sharing - Support the promotion of open science through publishing papers and open-source code Note: Reasonable accomodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned. Required Education and Experience - PhD in Computer Science, Applied Mathematics, Computational Biology, Statistics, Biostatistics or similar field; or equivalent combination of degree and experience - Minimum of 2 years postdoctoral / work experience - Demonstrated ability to design, implement and apply AI/ML models for the analysis of large-scale biological data Preferred Education and Experience - 2 - 5 years of experience developing and applying ML methods - Strong publication record of innovative scientific accomplishments (both individual and team) - Expertise in Python-based ML libraries and frameworks such as PyTorch, Jax, Pyro, NumPy, and Pandas. Solid understanding of statistical analysis, data preprocessing, feature selection, and model evaluation techniques - Experience building data pipelines to make biological data ML-ready, pipeline for model training and evaluation. Knowledge of data preprocessing, normalization, and integration techniques specific to biological and clinical datasets - Experience with data visualization and presentation of complex biological findings to both technical and non-technical audiences - Strong problem-solving skills and ability to develop innovative computational approaches to address complex biological questions Physical Demands - Fine motor movements in fingers/hands to operate computers and other office equipment Position Type/Expected Hours of Work - This role is currently able to work both remotely and onsite in a hybrid work environment. We are a Washington State employer, and the primary work location for all Allen Institute employees is 615 Westlake Ave N.; any remote work must be performed in Washington State. Travel - Attendance and participation in national and international conferences Additional Comments - **Please note, this opportunity offers relocation assistance** - **Please note, this opportunity may offer work visa sponsorship** Annualized Salary Range $167,800 - $209,700* * Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable. Benefits Employees (and their families) are eligible to enroll in benefits per eligibility rules outlined in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institutes Benefits Guide. It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities.

Role Description To continue to increase the reach of services and drive business growth through the application of modern data science and applied AI methods, Accuity is seeking a VP, Data Science & AI to lead a team of skilled resources in support of the strategic priority to become a data-driven and outcomes-oriented organization. The successful candidate will bring deep experience with: - Machine learning - Predictive modeling - Statistical analysis - Mathematical optimization - Algorithm development - Artificial intelligence - A passion for working with healthcare data This role leads Accuity’s internal data science team and serves as the technical owner and counterpart for external AI development partners, accountable for the methodology, evaluation rigor, and production performance of AI systems including generative and agentic LLM-based systems that Accuity deploys and must operate independently over time. Reporting to the Chief Technology Officer, this role is responsible for advancing Accuity’s data science and AI roadmap, governing predictive and generative/LLM-based systems, and ensuring AI solutions are measurable, auditable, scalable, compliant, and operationally effective in a PHI/HIPAA-regulated healthcare environment. Responsibilities - Strategy and AI Leadership - Serve as the company-wide leader for data science and applied AI initiatives, including predictive modeling, generative AI, LLM-based systems, and agentic workflows. - Build and maintain Accuity’s data science and AI roadmap in alignment with enterprise technology priorities, business growth goals, operational needs, and client value. - Identify opportunities across the business to apply data science and AI to improve clinical documentation integrity, coding accuracy, reimbursement optimization, quality outcomes, productivity, and decision support. - Develop business cases, success criteria, and return-on-investment measures for data science and AI initiatives. - Translate business, clinical, operational, and financial needs into clear technical requirements, solution approaches, implementation plans, and measurable outcomes. - AI Development, Evaluation, and Production Performance - Own evaluation-driven development, including ground-truth datasets, evaluation frameworks, and the metrics that determine whether AI systems are production-ready. - Define and compute the performance metrics that govern commercial and incentive terms, ensuring they are reproducible and auditable. - Monitor, manage, and optimize production performance for predictive and generative/LLM-based systems, including subgroup performance, quality trends, calibration, drift, and measurable business impact. - Lead rapid testing, deployment, validation, and iteration cycles to improve model and system performance over time. - Partner with data architecture, engineering, security, and operations teams to operationalize new models and AI systems into stable, scalable production workflows. - Maintain hands-on technical credibility through direct engagement in data analysis, model evaluation, solution design, technical review, and applied AI problem solving as needed. - External Partner and Vendor Technical Oversight - Serve as Accuity’s technical owner and counterpart for external AI development partners, including partners supporting generative AI, LLM-based, and agentic systems. - Provide technical oversight of external AI partners by validating their architecture, methodology, deliverables, and performance against defined milestones and production-readiness gates. - Hold external partners accountable for evaluation rigor, documentation, system performance, reproducibility, auditability, and contractual commitments. - Lead knowledge transfer from external partners to Accuity teams so delivered systems can be operated, maintained, evaluated, and improved without ongoing vendor dependency. - Collaborate with external AI partners, vendors, prospective clients, current clients, and the PE-partner Center of Excellence as needed to support strategic technology initiatives and active engagements. - AI Governance, Compliance, and Risk Management - Lead AI governance for clinical decision systems, including model risk, human-in-the-loop requirements, decision traceability and auditability, calibration, and PHI-safe model routing. - Ensure AI solutions are developed and operated in accordance with HIPAA, BAA, information security, privacy, compliance, and risk management requirements. - Establish governance processes for responsible AI use, including documentation standards, evaluation records, model/system performance monitoring, and escalation criteria. - Partner with legal, compliance, security, technology, clinical, and operational stakeholders to ensure AI systems are appropriate for use in regulated healthcare workflows. - Support AI implementation practices using secure and compliant platforms, including Azure AI Foundry, Azure ML, and related enterprise AI infrastructure. - Leadership and People Management - Hire, lead, develop a high-performing, results-oriented internal data science team. - Foster a growth-oriented, inclusive, accountable, and high-performance team culture. - Set clear priorities, expectations, performance measures, and development plans for team members. - Build team capabilities in applied AI, evaluation-driven development, ML and LLM operations, healthcare analytics, and production performance management. - Maintain effective communication, team alignment, and collaboration in Accuity’s fully remote work environment. - Cross-Functional Collaboration and Communication - Coordinate data science and AI initiatives across Technology, Product, Operations, Finance, Clinical, Compliance, Security, and executive leadership. - Communicate program performance, risks, tradeoffs, and business impact clearly to technical and non-technical audiences, including C-suite and executive stakeholders. - Partner with business operators and functional leaders to ensure AI solutions are designed for practical workflow adoption, measurable value, and long-term operational ownership. - Serve as a trusted technical advisor on AI methodology, vendor deliverables, production readiness, and responsible deployment. - Other Duties as Assigned - Perform other duties and support additional initiatives assigned by leadership. Qualifications - Bachelor’s degree in Computer Science, Information Technology, Business Administration, Applied Mathematics, Statistics, Data Science, Engineering, or a related discipline required. - Master’s degree in a related field preferred. - 8+ years of hands-on experience in data science, machine learning, or applied AI, including technical team leadership. - Production experience with predictive and generative AI / LLM-based systems. - Experience with AI/ML evaluation, monitoring, and governance. - Experience overseeing external AI/ML vendors. - Proficiency with Python, SQL, Azure AI/ML platforms, and related tools. - Healthcare analytics experience required; PHI/HIPAA-regulated environment experience strongly preferred. Core Competencies - Applied AI Leadership: Demonstrates strong judgment in selecting, evaluating, governing, and operationalizing predictive, generative, and LLM-based AI systems. - Evaluation Rigor: Uses measurable, reproducible, and auditable methods to assess model/system quality, readiness, risk, and business impact. - Strategic Thinking: Connects data science and AI capabilities to enterprise priorities, client outcomes, operational performance, and long-term scalability. - Technical Credibility: Engages deeply enough with architecture, methodology, metrics, and implementation details to guide internal teams and challenge external partners effectively. - Governance and Risk Orientation: Balances innovation with responsible AI practices, clinical decision support considerations, PHI/HIPAA requirements, auditability, and model risk management. - Executive Communication: Communicates complex technical concepts, performance results, risks, and recommendations clearly to executive, operational, technical, and non-technical audiences. - Stakeholder Management: Builds trust and alignment across Technology, Operations, Finance, Clinical, Compliance, Security, clients, vendors, and external partners. - People Leadership: Develops talent, sets clear expectations, provides coaching, drives accountability, and builds an inclusive, high-performing remote team. - Execution Discipline: Moves complex AI initiatives from concept to production through clear priorities, milestones, ownership, performance gates, and measurable outcomes. - Problem Solving: Uses analytical thinking, experimentation, and sound judgment to resolve ambiguous, complex, and cross-functional business and technology challenges. - Remote Work Effectiveness: Communicates proactively, documents decisions clearly, manages distributed collaboration effectively, and maintains alignment across a fully remote organization. Additional Requirements - Physical Requirements: The requirements described here are representative of those that must be met by an employee to successfully perform the essential functions of this job with or without reasonable accommodations. Unless otherwise indicated, Accuity positions require interaction with people and technology while either sitting or standing. Employees must be able to communicate via phone, email, etc. and sit for extended periods of time, with or without reasonable accommodations. Physical effort and exposure to physical risk are limited to that of an office role / environment. - Position and Employment Statement: While this job description is intended to be an accurate reflection of the job requirements, management reserves the right to modify, add or remove duties from a job and to assign other duties as necessary and at any time. All positions at Accuity Delivery Systems, LLC, are at-will employment, and a position description is not a guarantee of a job or of job responsibilities.

• Lead creative on enterprise engagements. • Set the creative bar. • Drive performance, not just aesthetics. • Shape the product. • Define the playbook.

• Lead end-to-end research initiatives that inform the design and direction of AI-powered products and features • Partner closely with product design, engineering, and product management to translate research insights into actionable design decisions • Define and evolve research methodologies for emerging AI interaction paradigms — including conversational AI, generative AI, and agentic systems • Conduct generative and evaluative research using a range of methods (interviews, usability studies, surveys, diary studies, concept testing, etc.) • Synthesize complex findings into clear, compelling narratives for executive and cross-functional audiences • Mentor and support other researchers, raising the craft across the team • Champion ethical AI design and contribute to frameworks for responsible AI research