We are seeking a talented Pharmacoepidemiologist to join our team to support projects at the NIH’s National Center for Advancing Translational Sciences (NCATS). In this role, you will collaborate with clinical and data scientists, methodologists, and software engineers to develop and execute exemplar causal inference studies. You will work as a subject matter expert to create a real-world evidence (RWE) methods decision tree; support master protocol development; and support development of gold-standard guides and methodologies for conducting best practices causal inference research. You will: design, execute, and analyze studies using the National Clinical Cohort Collaborative (N3C) data; review and recommend strategies for selecting study designs to answer causal inference questions; help establish, and implement appropriate analytic value sets. 

The ideal candidate for the Pharmacoepidemiologist position is a highly skilled professional with a Ph.D. in Pharmacoepidemiology, Epidemiology, Biostatistics, Causal Inference or related field. This person has experience with electronic health record and/or claims data and a strong understanding of observational study principles including: missing data handling methods; temporal research questions (cross-sectional, longitudinal); causal contrast of interest (e.g., intent-to-treat, per-protocol); effect measure of interest (e.g., risk ratio, hazard ratio); and estimands (e.g., average treatment effect, average treatment effect in the treated) of interest. Your research experience includes one or more of the following: target trial emulation, sequential trial analysis, marginal structural models, longitudinal matching, G methods, or equivalent causal inference methods, realized in multiple primary-author publications. Fluency with coding languages and tools is expected (i.e. SQL, Python, and R). 

Responsibilities: 

• Serve as SME for RWE causal inference studies.
• Design and execute studies with N3C EHR data considering the following: extent of missing data; missing data handling methods; temporal component of research studies (i.e. cross-sectional, longitudinal); the causal contrast of interest (e.g., intent-to-treat, per-protocol); the effect measure of interest (e.g., risk ratio, hazard ratio); and estimand (e.g., average treatment effect, average treatment effect in the treated) of interest.
• Develop and operationalize decision-support frameworks to standardize how causal inference studies are designed, executed, and evaluated across projects.
• Contribute to the development of reusable, platform-level components that support reproducible and scalable RWE generation.
• Review and recommend strategies for selecting individuals in clinical cohorts.
• Ensure the highest data quality and minimization of bias.
• Use tools and languages such as Python, R, SQL, and Spark.
• Help establish and implement appropriate analytic value sets.
• Collaboratively produce extensive documentation (i.e. through training material, research protocols, open data sources such as repositories and/or methodological and research papers).
• Work closely with data science and software developer teams to articulate technical methods and implement decision tool strategies.
• Extract and transform clinical data from clinical trial and observational studies.
• Participate in project meetings, provide updates on progress, challenges, and mitigation strategies.
• Actively engage in key relevant meetings and keep abreast of emerging best practices and methods. 

Qualifications 

Must haves: 

• Ph.D. in Pharmacoepidemiology, Epidemiology, Biostatistics or related field.
• Experience with the following: target trial emulation, sequential trial analysis, marginal structural models, longitudinal matching, G methods, or equivalent causal inference methods realized in at least 2 primary-author peer-reviewed publications, or equivalent.
• Experience developing or applying structured methodological frameworks to guide observational research.
• Ability to independently conduct statistical analysis with analytic software tools (e.g. Python, R, SQL, MatchIt/WeightIt, Matplotlib, Lifelines, SKLearn, GT-summary, statsmodels, Survey).
• Fluent in coding languages and tools (e.g. Python, R, SQL, Spark).
• Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary team. 

Nice to haves: 

• Experience with meta-analyses and/or systematic reviews.
• Expertise in best practices for statistical approaches, data wrangling and visualization.
• Knowledge of FAIR data principles.
• Clinical informatics experience. 

Axle is seeking a skilled Healthcare EHR Interoperability Specialist to join our Real-World Evidence team. In this role, you will be responsible for designing, implementing, and validating data mappings between diverse Electronic Health Record (EHR) clinical data models. You will leverage our proprietary Domain-Specific Language (DSL) to express and automate mappings, ensuring semantic accuracy and data quality across source and target systems.

This is a highly technical position at the intersection of clinical informatics, software engineering, and data quality. It’s well suited for someone who is equally comfortable interpreting clinical data specifications and developing robust QA test suites.

Clinical Data Mapping

• Analyze and interpret source and target EHR clinical data models (e.g. FHIR, OMOP, PCORNet) to understand structural and semantic differences.
• Design and implement accurate, well-documented mappings between CDMs using our internal DSL.
• Apply knowledge of clinical terminologies (SNOMED CT, LOINC, ICD-10, RxNorm, etc.) to resolve semantic gaps during mapping.
• Work with FHIR (R4/R5), HL7 v2/v3, and OMOP CDM as source or target data models.
• Align mappings with US Core Data for Interoperability (USCDI) requirements and ONC guidelines.

DSL Development & Usage

• Learn and become proficient in our proprietary mapping DSL within the onboarding period.

• Author, review, and maintain DSL-based mapping scripts with a focus on readability and reusability.

• Collaborate with the DSL platform team to report issues, suggest improvements, and contribute to language documentation.

• Develop reusable mapping components and patterns to promote consistency across projects.

Quality Assurance & Testing

• Write and execute unit and integration tests for all mapping logic to ensure correctness and completeness.

• Develop test datasets and validation frameworks for end-to-end mapping pipelines.

• Perform data profiling and discrepancy analysis to identify anomalies in source data.

• Maintain mapping traceability documentation and participate in peer reviews.

• Investigate and resolve data quality issues escalated from downstream consumers.

Collaboration & Documentation

• Partner with clinical SMEs, EHR vendors, and implementation teams to clarify ambiguous data definitions.

• Produce clear mapping specifications, data dictionaries, and transformation logic documentation.

• Participate in sprint planning, stand-ups, and retrospectives as part of an agile delivery team.

• Support data governance activities including metadata management and lineage tracking.

Education

• Bachelor's degree or higher in Health Informatics, Biomedical Informatics, Computer Science, Information Systems, or a related field.

• Advanced degree (MS/PhD) or relevant certifications (RHIA, CHDA, CPHIMS) are a plus.

Clinical Data Model Expertise

• Deep, hands-on experience with FHIR resource modeling, profiling, and implementation guides (US Core, etc.). FHIR (HL7 R4/R5):

• Practical experience transforming EHR data into OMOP for research or analytics use cases.

• Familiarity with the PCORnet Common Data Model for clinical research networks.[KH1] [KH2] 

• Working knowledge of US Core Data for Interoperability (USCDI), ONC regulations, and CMS interoperability rules. US CDEs:

• Familiarity with additional CDMs such as i2b2, Sentinel, or proprietary EHR schemas is advantageous.

Clinical Terminology Knowledge

• Proficiency with SNOMED CT, LOINC, ICD-10-CM/PCS, RxNorm, and NPI in the context of data mapping.

• Experience using terminology servers (e.g., NLM VSAC, Ontoserver, HAPI FHIR Terminology) to resolve concept mappings.

• Understanding of value set authoring and maintenance practices.

Technical & Programming Skills

• Proficiency in at least one scripting/programming language: Python, Java, or JavaScript/TypeScript.

• Strong SQL skills for data extraction, profiling, and transformation across relational databases.

• Familiarity with version control (Git) and collaborative development workflows (pull requests, code review).

• Ability to quickly learn and become productive in a custom DSL — prior experience with any DSL, query language (e.g., FHIRPath, OCL, XQuery), or configuration language is strongly preferred.

QA & Testing Skills

• Experience writing automated test cases for data transformation logic (unit, integration, and regression tests).

• Proficiency with testing frameworks relevant to your primary language (e.g., pytest, JUnit, Jest).

• Data validation skills: record counts, field-level completeness checks, referential integrity, and semantic equivalence testing.

• Familiarity with CI/CD pipelines and automated testing workflows.

Soft Skills & Work Style

• Detail-oriented with a strong commitment to data accuracy and clinical fidelity.

• Able to manage ambiguity — comfortable making informed mapping decisions when source documentation is incomplete.

• Strong written and verbal communication skills; able to explain technical mapping decisions to clinical stakeholders.

• Self-directed learner with the ability to ramp up quickly on new systems and standards.

• Prior experience working at a healthcare system, HIE, health IT vendor, or CRO.

• Hands-on experience with one of the major EHR platforms: Epic Clarity/Caboodle, Cerner Millennium, Allscripts, or athenahealth.

• Experience contributing to open-source health data projects (e.g., HAPI FHIR, Inferno, OHDSIverse).

• Knowledge of HIPAA, 21st Century Cures Act, and related data privacy and compliance requirements.

Axle is seeking a visionary and operationally astute Associate Director of Artificial Intelligence, Modeling, and Data to lead our charge at the frontier of biomedical innovation. In this pivotal leadership role, you will architect the future of AI-driven discovery, bridging the gap between advanced computational science and life-saving clinical research.

Sitting at the nexus of our Data Science and Biomedical Research divisions, you will oversee the strategic development and deployment of high-performance AI/ML solutions that empower federal health agencies, including the National Institutes of Health (NIH) and the National Center for Advancing Translational Sciences (NCATS). You will be the technical conscience and strategic driver for a diverse portfolio of projects, ranging from the optimization of our open-source Polus platform to the implementation of Generative AI pipelines for drug discovery and high-dimensional data analysis.

This is not merely a management role; it is a mission-critical position for a leader who is passionate about the "public good" of science. You will galvanize interdisciplinary teams of PhD scientists, machine learning engineers, and software architects to solve complex health challenges, such as pandemic preparedness and rare disease treatment. As an Associate Director, you will combine deep technical expertise with executive presence, serving as a trusted advisor to government stakeholders and a key architect of Axle’s long-term growth in the federal health IT sector.

Core Responsibilities:

1. Strategic Leadership of the AI/ML Roadmap

• Visionary Architecture: Architect and execute a comprehensive AI/ML strategy that aligns Axle’s technical capabilities with the NIH Strategic Plan for Data Science (2025–2030). You will define the long-term vision for integrating Generative AI, Large Language Models (LLMs), and Agentic Workflows into federal research environments, moving beyond static analysis to active, AI-assisted discovery.

• Platform Evolution (Polus): Spearhead the evolution of the Polus platform, transitioning it from a robust image analysis tool into a fully integrated, multi-modal research ecosystem. You will oversee the roadmap for new feature development, ensuring scalability, security, and interoperability across cloud environments (AWS/GCP/Azure) using containerized architectures (Docker/Kubernetes).

• AI Governance & Compliance: Establish and enforce rigorous AI Governance frameworks. You will operationalize the NIST AI Risk Management Framework (RMF) across all projects to ensure fairness, interpretability, and compliance with federal ethical standards. You will lead "Gap Analysis" and "Risk Management" exercises to ensure all AI deployments are trustworthy and transparent.

2. Oversight of Complex Modeling & High-Dimensional Data Pipelines

• Petabyte-Scale Engineering: Direct the design and implementation of high-throughput data pipelines capable of ingesting and analyzing petabyte-scale datasets (genomics, proteomics, EHR). You will ensure these systems adhere to FAIR data principles (Findable, Accessible, Interoperable, Reusable), facilitating seamless data sharing across NIH institutes and global research centers.

• Translational Science De-risking: Oversee the development of predictive models for translational science, focusing on "de-risking" drug discovery and clinical trial design. This involves guiding technical teams in the application of deep learning techniques to identify molecular targets, predict therapeutic outcomes, and simulate clinical scenarios (Digital Twins).

• Operational Excellence (MLOps): Optimize MLOps and DevSecOps processes to ensure the rapid, secure deployment of models from prototype to production. You will champion a culture of "automation first," reducing time-to-insight for researchers by streamlining the transition from Jupyter notebooks to containerized, cloud-native services.

3. Business Development & Federal Growth (Capture Support)

• Lead Solution Architect: Partner with the Growth and Capture teams to drive new business acquisition. You will serve as the Lead Solution Architect for major proposal efforts ($50M+), authoring technical volumes, developing win themes, and creating compelling solution graphics that demonstrate Axle’s technical differentiation.

• Proposal Authorship: Personally write key sections of technical proposals, including the "Technical Approach," "Staffing Plan," and "Risk Mitigation" volumes. You will translate complex agency requirements into winning narratives that score highly with federal evaluators.

• Client Liaison: Galvanize relationships with key federal stakeholders (Project Officers, CIOs, Lab Chiefs). You will act as the primary technical liaison, translating complex agency requirements into deliverable technical solutions and presenting these visions in competitive "Black Hat" sessions and oral presentations.

4. Mentorship of Data Scientists & Engineers

• Interdisciplinary Team Building: Cultivate a high-performance, interdisciplinary team culture. You will manage and mentor a diverse group of data scientists, bioinformaticians, and software engineers, fostering an environment of psychological safety where "expert" scientific knowledge seamlessly integrates with "agile" engineering practices.

• Continuous Learning: Drive continuous learning and upskilling initiatives. You will establish internal "Communities of Practice" for AI and Data Science, ensuring that Axle’s workforce remains at the bleeding edge of technologies like Graph Neural Networks and Federated Learning.

• Democratization of AI: Democratize access to AI tools within the client environment. You will lead efforts to create "low-code/no-code" interfaces and training programs that empower non-technical NIH researchers to utilize advanced analytics independently.

Required Qualifications: To effectively lead in this high-stakes environment, candidates must possess a rare combination of advanced academic training and battle-tested industry experience.

Education:

• Ph.D. in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related quantitative discipline is highly preferred to ensure peer-level credibility with NIH scientists.

• Alternatively, a Master’s degree in one of the above fields with exceptional, demonstrated leadership experience in a federal or research-intensive setting will be considered.

Experience:

• 8–10+ years of progressive experience in data science, AI/ML engineering, or computational biology, with a focus on high-dimensional data.

• 3–5+ years of leadership experience managing cross-functional teams (e.g., managing both PhD researchers and software developers) in a matrixed organization.

• Federal Contracting Experience: Demonstrated experience with Federal Business Development, including writing technical proposals and supporting capture activities for contracts valued at $15M+.

• Regulatory Experience: Proven track record of delivering complex AI/ML solutions in a regulated environment, with specific familiarity with HIPAA, FedRAMP, or NIST AI RMF compliance.

Technical Skills: The Assistant Director must possess deep, hands-on technical fluency to command respect from engineering teams and validate architectural decisions.

• Core AI/ML: Expert-level understanding of Deep Learning frameworks (PyTorch, TensorFlow), Classical Machine Learning (Scikit-Learn), and Generative AI architectures (Transformers, LLMs, RAG).

• Languages: Proficiency in Python (primary) and R (secondary); familiarity with Java or C++ (for Polus backend optimization) is a strong plus.

• Cloud Architecture: Extensive experience with Cloud-Native AI pipelines on AWS (SageMaker, HealthLake), GCP (Vertex AI, BigQuery), or Azure. Knowledge of the NIH STRIDES initiative and cloud economics is essential.

• Data Engineering: Mastery of big data technologies (Spark, Databricks) and workflow orchestration tools (Airflow, Nextflow, Cromwell).

• MLOps & DevOps: Strong knowledge of containerization (Docker, Kubernetes), CI/CD pipelines (GitHub Actions, Jenkins), and model monitoring/governance tools.

• Visualization & Platforms: Experience with advanced visualization tools (DeepZoom, WebGL) and platform development (building APIs, microservices).

Preferred Skills: Candidates with the following specific experiences will be given immediate priority:

• NIH Ecosystem Experience: Direct experience working with NIH, NCATS, NIAID, or similar federal health agencies. Understanding of the specific data challenges within the federal health sector is highly valued.

• Open Source Leadership: Contributions to or leadership of open-source scientific software projects. Specific familiarity with the Polus platform or the National COVID Cohort Collaborative (N3C) data enclave is a distinct advantage.

• NIST AI RMF Practitioner: Demonstrated experience implementing the NIST AI Risk Management Framework (Map, Measure, Manage, Govern) in a real-world setting.

• Domain Expertise: Specialized knowledge in High-Content Imaging, Cheminformatics, Genomics, or Real-World Data (RWD) analytics.

The Director of Data Solutions is the senior technical delivery leader for data platforms, AI/ML solutions (including GenAI), and advanced modeling/simulation capabilities. This leader owns the “how and when” of building reusable, enterprise-grade capabilities that turn complex, multi‑modal data into trusted products and measurable outcomes.

In practice, this role:

• Sets technical strategy and reference architecture across modern data stacks and cloud environments.
• Leads cross-functional teams (data engineering, ML engineering, applied science, software engineering) to ship and operate production systems.
• Establishes an organization-wide modeling and simulation practice that ensures reproducibility, compute strategy, and strong quality standards.

KEY RESPONSIBILITIES

Technical strategy & architecture

• Define reference architectures and technical standards for data/AI platforms (security, scalability, reliability, cost governance, developer experience).

• Own platform modernization plans and technical debt reduction sequencing.

• Make build/buy/partner decisions and establish patterns that can be reused across programs.

Interoperability, harmonization & data quality

• Lead delivery of repeatable ingestion and transformation pipelines with testing, validation, and change control.

• Own harmonization capabilities (terminology translation, unit normalization, episode building) as production services with documentation and quality dashboards.

• Partner with governance and stakeholders to define “minimum acceptable quality” and publish transparent quality measures.

AI/ML and GenAI solution delivery

• Lead delivery of production AI/ML solutions (NLP, CV, predictive models, representation learning) and deploy them with evaluation and monitoring.

• Own GenAI patterns and platforms (RAG, agentic workflows, human-in-the-loop review, traceability, privacy safeguards) as reusable services.

• Establish model lifecycle governance: approvals, audits (as needed), drift monitoring, incident response, and continuous improvement.

Real‑world evidence enablement engines

• Build reusable “engines” for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple data sources.

• Staff and support analysis pods for time-sensitive, high-stakes deliverables with rigorous QC and reproducibility practices.

Simulations & modeling practice leadership

• Define the modeling/simulation practice charter: scope, service model, standards, compute strategy (HPC/cloud), and hiring/partnering plan.

• Lead simulation/modeling teams directly or via domain SMEs; ensure reproducible workflows and high quality bars.

• Identify and prioritize high-value hybrid ML+simulation opportunities.

Privacy, security & operational excellence

• Partner with security/privacy to implement strong access controls, auditability, and (where needed) privacy-preserving approaches.

• Establish operational excellence: release management, observability, on-call/incident processes (as appropriate), and runbooks.

People leadership & culture

• Hire, grow, and retain a high-performing organization; create clear roles, career paths, and performance expectations.

• Build a culture of “research-grade rigor + production-grade discipline,” emphasizing accountability, documentation, and sustainability.

REQUIRED QUALIFICATIONS

• 6+ years in data science, ML engineering, data platform engineering, applied research engineering, or closely related fields

• 3+ years leading multi-disciplinary teams.

• Demonstrated success delivering production data/AI platforms (not only analyses), including architecture, delivery planning, and operational ownership.

• Strong familiarity with modern data stacks and cloud delivery (distributed compute, ETL/ELT, data quality tooling, MLOps/LLMOps concepts).

• Ability to translate ambiguous stakeholder needs into shipped products and measurable outcomes.

• Strong people leadership: recruiting, coaching, performance management, org design.

• Comfort operating in regulated and high-governance environments (privacy, compliance, access control).

PREFERRED QUALIFICATIONS

• Healthcare data platform experience, especially interoperability/harmonization at scale (OMOP/FHIR/PCORNet/CDISC) and clinical terminology systems.

• Experience shipping GenAI solutions with governance (PII handling, traceability, human review, evaluation, monitoring).

• Experience with privacy-preserving ML patterns (federated learning/inference) and/or sensitive data platforms.

• Experience leading simulation/modeling initiatives (scientific computing, HPC workflows, domain simulations) and partnering effectively with scientific SMEs.

• Track record of publications, open-source leadership, or scientific impact.

Axle is hiring a Junior Data Science Engineer to support projects across the federal space, including the National Center for Advancing Translational Sciences (NCATS) within the National Institutes of Health (NIH). You will work alongside a collaborative team of developers, data scientists, and subject matter experts to build and enhance data-driven applications, APIs, and analytics tools that support scientific research and data exploration.

This role provides hands-on experience with modern web technologies, data pipelines, and cloud-based environments, contributing to systems that enable interactive data analysis and visualization.

Qualifications:

• Bachelor’s Degree (or currently pursuing) in Computer Science, Data Science, Bioinformatics, Statistics, Engineering, or related field

• Experience or coursework in JavaScript/TypeScript and/or Python

• Exposure to full-stack development concepts (frontend and backend)

• Familiarity with Node.js and Angular or similar frameworks

• Understanding of RESTful APIs and web services

• Exposure to version control systems (Git)

• Familiarity with Agile/SCRUM development methodologies

• Basic understanding of testing frameworks and debugging practices

Relevant Skills:

• Strong problem-solving skills and attention to detail

• Ability to communicate clearly and effectively (written and verbal)

• Ability to work both independently and in a team environment

• Adaptability in a fast-paced environment with changing priorities

• Interest in data science, analytics, or machine learning concepts

• Familiarity with data visualization or dashboarding tools is a plus

Responsibilities:

• Assist in building and maintaining data-driven applications using JavaScript/TypeScript and/or Python

• Support development of web applications using Node.js and Angular

• Contribute to the design and implementation of RESTful APIs

• Participate in testing, debugging, and enhancing existing codebases

• Help implement new features and performance improvements

• Collaborate in Agile development cycles, including sprint planning, stand-ups, and code reviews

• Support development of data pipelines, dashboards, and visualization tools

• Gain hands-on experience working in NIH environments across multiple sites

Axle is seeking a Senior Software Engineer – Biomedical Informatics to join our vibrant team at the National Institutes of Health (NIH) supporting the National Center for Advancing Translational Sciences (NCATS) located in Rockville, MD.

We are seeking a Senior Software Engineer with deep experience in scientific computing and biomedical informatics to lead the development and maintenance of software systems that support translational research. This role involves architecting and operating Python-based applications, managing cloud infrastructure, and working with biomedical data standards and machine learning models.

You will work across multiple project streams as part of a cross-functional team that includes data scientists, researchers, and other engineers. A key aspect of this role is partnering directly with subject matter experts — scientists and researchers who understand the problem domain but may not have a technical background — to elicit requirements, translate research needs into well-defined technical solutions, and ensure that delivered software meets their real-world needs. The role requires a seasoned, self-directed engineer who can make sound architectural decisions, mentor junior team members, take ownership of technical components end-to-end, and deliver iteratively in a collaborative research environment.

Key Responsibilities

• Architect, develop, and maintain Python-based scientific software, including client/server libraries, CLI tools, and containerized applications.

• Lead technical design decisions and establish best practices for code quality, testing, and deployment.

• Provision, deploy, monitor, and troubleshoot cloud services on AWS, including managing IAM roles, deployment pipelines, and S3 storage.

• Work with embedding models and vector databases to support NLP and semantic search capabilities.

• Design and maintain OMOP-compliant PostgreSQL databases, including vocabulary management and incremental data updates.

• Develop and maintain data migration and extraction scripts, with appropriate logging and verification for completeness.

• Conduct integration testing with external systems and evaluate model performance against large-scale datasets.

• Drive documentation, testing, and CI/CD workflows to support reliable software delivery.

• Engage directly with non-technical subject matter experts to gather requirements, clarify goals, and validate that delivered solutions align with research objectives.

• Translate high-level research needs into actionable technical specifications and development plans.

• Mentor junior engineers and contribute to a culture of technical excellence.

• Participate in and help facilitate standups, sprint planning, and cross-team review meetings across multiple project streams.

Required Skills & Qualifications

• 5+ years of professional experience in software development.

• Proficiency in Python for application development, scripting, and tooling.

• Demonstrated experience with machine learning models.

• Demonstrated experience making architectural decisions and owning technical direction for software projects.

• Experience with AWS (IAM, S3, deployment, general infrastructure management).

• Experience with PostgreSQL and relational database design.

• Experience with Docker and containerization workflows.

• Strong command of Linux development environments.

• Experience with Git/GitHub workflows, including branching, pull requests, and code review practices.

• Proven ability to work independently, prioritize across concurrent workstreams, and drive projects to completion with minimal oversight.

Preferred Qualifications

• Experience with NLP.

• Experience with GPU computing (CUDA/NVIDIA).

• Familiarity with the OMOP Common Data Model or biomedical ontologies and vocabularies.

• Experience working with healthcare records, patient data, or clinical datasets.

• Experience with PostgreSQL extensions such as pgvector for vector similarity search.

• Background in biomedical informatics, translational research, or health sciences.

• Familiarity with Jira or similar project management tools.

Interpersonal Skills

• Strong written and verbal communication skills, including the ability to convey technical concepts to non-technical collaborators and translate domain expertise into software requirements.

• Skilled at requirements elicitation — able to ask the right questions, listen actively, and bridge the gap between what subject matter experts need and what engineering can deliver.

• Ability to work effectively across multiple teams and project streams with different leads, priorities, and cadences.

• Comfortable operating in a large, cross-functional team environment where coordination and alignment are essential.

• Proactive in communicating progress, raising blockers, and seeking input from teammates and stakeholders.

• Tactful and professional when engaging with clients and external partners; able to navigate sensitive situations and escalate appropriately.

• Receptive to feedback and committed to iterative improvement.

What We Emphasize

• Proactive communication and early delivery over perfection

• Ownership of well-scoped technical components with clear rationale for decisions

• Self-driven initiative in managing code quality, versioning, and task completion

• Willingness to research and adopt new technologies and libraries

• Collaboration and mentorship within a multidisciplinary team

Overview

The AI/ML Scientist/Developer will develop innovative computational models to predict and optimize organoid growth and differentiation protocols. This position represents a unique opportunity to apply cutting-edge machine learning techniques to advance organoid standardization and contribute to the development of predictive models for tissue engineering applications.

Responsibilities

• The successful candidate will design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.
• They will develop in silico models that can simulate organoid growth dynamics and identify optimal conditions for reproducible organoid generation.
• The role involves creating feedback loops between experimental validation and computational prediction to iteratively improve protocol standardization.
• Collaboration with experimental teams to design validation experiments and with data scientists to integrate multi-modal datasets will be essential components of this position.

Required Qualifications

• Candidates must possess a Master's degree or PhD in computer science, engineering, applied mathematics, or a related field with demonstrated experience in AI/ML model development.
• Strong programming skills in Python, R, or similar languages are required, along with experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Previous experience applying computational methods in biological laboratory settings is necessary, with knowledge of experimental design principles and biological data types.

Preferred Qualifications

• Experience with organoid systems or tissue engineering applications is highly desirable.
• Knowledge of differential equations, systems biology modeling, and experience with deep learning approaches for biological applications will be considered strong assets.
• Familiarity with cloud computing platforms and containerization technologies is preferred.

Overview:

The Standardized Organoid Model Center is an NIH-funded initiative dedicated to advancing organoid research through the development of validated, reproducible, and well-characterized organoid models. The center brings together interdisciplinary teams of researchers to establish standardized protocols, develop quality control measures, and create resources that will benefit the broader organoid research community.

Responsibilities:

• The successful candidate will design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive organoid characterization profiles.
• They will develop predictive models that assess organoid quality and functionality based on molecular signatures and identify biomarkers that correlate with successful organoid development.
• The role involves creating computational tools for comparing organoid characteristics across different protocols and laboratories to support standardization efforts.
• Collaboration with experimental teams to validate computational predictions and translate findings into actionable protocol improvements will be essential.

Required Qualifications:

• Candidates must hold a PhD in computational biology, bioinformatics, computer science, or a related quantitative field with demonstrated experience applying AI/ML methods to biological systems.
• Strong programming skills in Python and R are required, along with experience with machine learning frameworks and statistical analysis packages.
• Knowledge of multi-omics data integration techniques and experience with biological pathway analysis are necessary.
• Familiarity with cloud computing platforms and high-performance computing environments is required.

Preferred Qualifications:

• Previous experience working with organoid datasets or tissue engineering applications is highly desirable.
• Experience with deep learning approaches for biological data, knowledge of systems biology principles, and familiarity with network analysis methods will be considered valuable assets.
• Experience with collaborative research projects and manuscript preparation is preferred.