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.

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 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.

Axle is seeking a highly ambitious, adaptable, structured, and detail-oriented Bioinformatics Scientist to join our vibrant team at the National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), Division of Intramural Research (DIR), Research Technologies Branch (RTB), Integrated Data Sciences Section (IDSS) in Bethesda, MD. 

 
Responsibilities:

• Address bioinformatics, scientific computing, and data analysis needs of users within the Division of Intramural Research (DIR) at NIAID and their collaborators by lending expertise in the proper statistical design of biological experiments involving high-throughput genome-scale technologies, data mining and knowledge discovery techniques, automated workflow development, statistical and scientific experimental design consulting.
• Provide bioinformatics support to include somatic and germline variant calling and analysis, single-cell and bulk gene expression analysis, de novo assembly of genomes and transcriptomes, comparative genomics of model and non-model organisms, proteomics, ChIP-seq, ATAC-seq, and the development of high-throughput pipelines for the analysis of the above data modalities.
• Provide programming and troubleshooting support to the Research Technologies Branch (RTB) of the National Institute of Allergy and Infectious Diseases (NIAID) and collaborating Institutes in the dissemination of research data.
• Perform computational data analysis on genomic and clinical research data.
• Work with staff on scientific programming and experimental design. Provide statistical support/analysis on research data.
• Contribute to and co-author scientific publications in leading peer-reviewed journals.
• Contribute to the development of a scalable and flexible resource to provide NGS bioinformatics, structural biological and computational, machine learning data support to NIAID scientists and investigators.
• It is expected that the bioinformatics scientist will need strong skills in the following:
  • NGS analysis (whole-exome, whole-genome, RNA-seq, ChIP-Seq, both long- and short-read technologies, etc.)
  • Statistical modeling
  • Scripting
  • Biological interpretation

Specific Qualifications:

• Ability to obtain and maintain a Public Trust Security clearance
• Minimum Bachelor’s degree in related field, Master’s or Ph.D. preferred
• 4+ years of experience in bioinformatics
• 2+ years of experience developing and maintaining production bioinformatics pipelines
• Expert in two (2) of the following languages: Python, R, C/C++, sh/bash
• Experience building images using one (1) of the following: Docker, Podman, Singularity/Apptainer
• Expert in one (1) pipeline framework: Snakemake, Nextflow, CWL/WDL
• Expertise building technical and user documentation for tools, pipelines, and applications
• Experience building pipelines locally on-premise using available HPC infrastructure
• Experience building pipelines/applications in the Cloud with AWS/GCP/Azure is a plus
• Experience building applications using one (1) of the following frameworks is a plus: Django, Flask, RShiny, Electron, Flutter, React
• Experience working in a laboratory setting is a plus but not required

About the Role

Axle is looking for an experienced Electron Microscopy Specialist to join our team supporting research at NIAID’s Rocky Mountain Laboratories in Hamilton, Montana. In this role, you’ll work closely with scientists in a collaborative, fast-paced environment, helping generate high-quality imaging data that supports infectious disease research.

This is a hands-on position for someone who enjoys both the technical side of electron microscopy and the collaborative nature of working with different research groups. If you have strong immuno-EM experience and are excited about learning and applying newer approaches like CLEM, FIB-SEM, and cryo-EM, this could be a great fit.

What You’ll Do

• Prepare biological samples for electron microscopy (fixation, embedding, sectioning, staining) and perform imaging and analysis.

• Design and conduct immuno-electron microscopy (immuno-EM) experiments.

• Operate and help maintain TEM and/or SEM instruments, including troubleshooting when needed.

• Work closely with NIAID investigators to plan experiments, interpret results, and solve technical challenges.

• Help bring new capabilities online, including techniques like CLEM, FIB-SEM, and cryo-EM.

• Keep clear, well-organized records in line with NIH standards for rigor and reproducibility.

• Train and support other lab members or facility users in EM techniques.

• Contribute to the overall operation of the lab, including equipment upkeep and safety compliance.

What We’re Looking For

• Degree in a relevant field (Cell Biology, Structural Biology, Biophysics, or similar).

• At least 5 years of relevant job experience in biological electron microscopy.

• Solid understanding of EM sample preparation and imaging workflows.

• Strong experience with immuno-electron microscopy.

• Experience operating and troubleshooting EM instruments.

• Someone who communicates well, works collaboratively, and is comfortable supporting multiple projects.

Nice to Have

• Experience working in a shared resource or core facility.

• Experience with, or strong interest in learning:

• Correlative Light and Electron Microscopy (CLEM)

• Focused Ion Beam-SEM (FIB-SEM)

• Cryo-electron microscopy (cryo-EM)

• Familiarity with image analysis tools.

What Makes Someone Successful in This Role

• You’re a team player who enjoys working with a range of scientists and projects.

• You’re curious and motivated to learn new techniques and technologies.

• You’re organized, detail-oriented, and take pride in producing high-quality data.

• You can balance multiple priorities without losing track of the details.

The Site Reliability Engineer role centers on modernizing and consolidating a complex multi-cloud environment across AWS, Azure, and GCP, building a scalable, secure, and observable platform from the ground up using Kubernetes, AI/ML infrastructure, and zero-trust principles. You'll combine DevOps and SRE practices to support mission-driven scientific and clinical programs, emphasizing automation, reliability, compliance, and proactive monitoring while enabling innovation through AI-driven tooling. The team culture is highly collaborative and growth-oriented, valuing experimentation, continuous learning, and cross-functional leadership, with opportunities to shape future multi-cloud and platform engineering solutions.

Responsibilities:

• Design and implement enterprise-grade monitoring and observability frameworks (metrics, logs, traces) across distributed systems using enterprise Splunk, Grafana and Open-telemetry tools

• Establish and manage SLIs, SLOs, and error budgets to drive reliability improvements 

• Develop and maintain real-time asset inventory systems across cloud, on-prem, and hybrid environments 

• Automate workload onboarding and offboarding processes, ensuring standardization and governance 

• Track system ownership, dependencies, and lifecycle states for operational transparency

• Build proactive detection mechanisms using AIOps and intelligent alerting to minimize incident impact

• Design and operate scalable, resilient, and secure infrastructure platforms across cloud and hybrid environments 

• Implement automated compliance tracking and enforcement aligned with organizational and regulatory standards (e.g., NIST, FISMA, FedRAMP) 

• Embed ITIL processes (incident, change, problem, configuration management) into SRE workflows 

• Build and maintain automated deployment environments and pipelines that enforce security, compliance, and operational standards 

• Develop “golden paths” and standardized platform templates for consistent workload deployment 

• Automate provisioning, patching, configuration management, and environment lifecycle 

• Leverage AI/ML coding assistants and vibe coding practices to rapidly develop automation scripts, tools, and internal platforms 

• Integrate AI-driven tooling into DevOps pipelines for code quality, security scanning, and operational insights 

• Lead adoption of AI-enhanced SRE practices, including intelligent remediation and predictive operations

• Champion DevOps and SRE practices including Infrastructure as Code, CI/CD, observability, and reliability engineering 

• Build developer-friendly platforms (“golden paths”) that simplify deployments, reduce friction, and improve velocity 

• Enable and optimize infrastructure for AI/ML workloads, including data pipelines, storage systems, and inference environments, GPU-enabled and high-performance compute workloads 

• Build and manage containerized and orchestrated platforms (Docker, Kubernetes) 

• Support cloud migration, modernization, and platform standardization initiatives 

• Ensure systems meet security, compliance, backup, and disaster recovery requirements 

• Evangelize and promote best practices in DevOps, SRE, and platform engineering to developer communities

• Stay abreast of new technologies in your areas but not limited to AIOps, MLOps, cloud computing & deployment, site reliability engineering, infrastructure automation, security best practices, data engineering etc. 

   

Requirements:

• Must have total of 6+ experience DevOps / SRE roles with monitoring and observability tools (Prometheus, Grafana, ELK, or cloud-native equivalents) for on-prem and cloud hosted workloads.

• Must have 4+ years of Hands-on Linux experience that includes Ubuntu/CentOS/Red Hat operating systems, containers, dependency management and administration support

• Must have 4+ years of experience automating Infrastructure-as-Code (IaC) deployments to one of the following cloud platforms Amazon AWS, Google GCP and Microsoft Azure

• Must have 4+ years with CI/CD and automation tools such as Terraform, Ansible, Chef, Puppet, Jenkins, GitHub Actions

• Strong scripting skills (Python, Bash, PowerShell or similar)

• Must be proficient using vibe coding and coding assistants to develop scripts, tools and applications for the DevOps and SRE use cases

• Must have proficiency to debug or troubleshoot and/or deploying SQL and/or NoSQL databases, object storage, web servers, open-source programming stack for Node.JS, R, Python, .NET Core, Java is desired but not mandatory

• Must be willing to learn new technologies, adopt and adapt to emerging technologies or needs from a project to a project

• Cloud certifications is preferred 

• Certifications in Grafana, Splunk, Docker, Kubernetes is preferred but optional

   

Axle is seeking a Computer Programmer to build and optimize scalable cloud and hybrid platforms using DevOps and SRE practices. This role focuses on automation, CI/CD, and supporting modern workloads like AI/ML, with an emphasis on reliability and efficiency.

Responsibilities:

• Design and operate scalable, resilient, and secure infrastructure platforms across cloud and hybrid environments 

• Champion DevOps and SRE practices including Infrastructure as Code, CI/CD, observability, and reliability engineering 

• Build developer-friendly platforms (“golden paths”) that simplify deployments, reduce friction, and improve velocity 

• Enable and optimize infrastructure for AI/ML workloads, including: 

  • Data pipelines and storage systems 

  • Model training and inference environments 

  • GPU-enabled and high-performance compute workloads 

• Develop and maintain automated CI/CD pipelines for applications, data, and ML workflows 

• Implement observability frameworks (metrics, logs, traces) to ensure system health and performance 

• Define and manage SLOs, SLIs, and error budgets to drive reliability improvements 

• Lead incident response, root cause analysis, and postmortems with a focus on continuous improvement 

• Automate provisioning, configuration, patching, and environment lifecycle management 

• Build and manage containerized and orchestrated platforms (Docker, Kubernetes) 

• Support cloud migration, modernization, and platform standardization initiatives 

• Ensure systems meet security, compliance, backup, and disaster recovery requirements 

• Evangelize DevOps practices to development community on the AI driven tool integrations for  code standards, qualitative tools, security tools in the shift-left automation

• Mentor engineers and promote best practices in DevOps, SRE, and platform engineering

• Stay abreast of new technologies in your areas but not limited to AIOps, MLOps, cloud computing & deployment, site reliability engineering, infrastructure automation, security best practices, data engineering etc. 

Requirements:

• Must have 6+ years of Hands-on Linux experience that includes specific technical experience with Ubuntu/CentOS/Red Hat operating systems, containers, dependency management and administration support

• Must have 4+ years of experience automating Infrastructure-as-Code (IaC) deployments to one of the following cloud platforms Amazon AWS, Google GCP and Microsoft Azure

• Must have 4+ years in DevOps / SRE roles supporting production systems

• Must have 4+ years with CI/CD and automation tools such as Terraform, Ansible, Chef, Puppet, Jenkins, GitHub Actions

• Strong scripting skills (Python, Bash, PowerShell or similar)

• Experience with monitoring and observability tools (Prometheus, Grafana, ELK, or cloud-native equivalents)

• Must be proficient using vibe coding and coding assistants to develop scripts, tools and applications for the DevOps and SRE use cases

• Must have proficiency to debug or troubleshoot and/or deploying SQL and/or NoSQL databases, object storage, web servers, open-source programming stack for Node.JS, R, Python, .NET Core, Java is desired but not mandatory

• Must be willing to learn new technologies, adopt and adapt to emerging technologies or needs from a project to a project

• Cloud certifications is preferred 

• Certifications in Docker, Kubernetes, Linux or Networking (CCNA or CCNP or similar) is preferred but optional

Axle is seeking an IT Operations Technical Lead to oversee and optimize hybrid cloud and on-premise infrastructure. This role combines hands-on technical leadership with ITIL-based operations, automation, and incident management. The ideal candidate brings deep Linux expertise and a focus on reliability, scalability, and modern workloads like AI/ML.

Responsibilities:

• Lead and manage IT operations aligned with ITIL processes including Incident, Problem, Change, and Release Management 

• Provide hands-on leadership in managing Linux and Windows environments across cloud and on-premises infrastructure 

• Own and drive incident response, root cause analysis, and service restoration for mission-critical systems 

• Design, build, and maintain golden images, patching strategies, and system hardening standards

• Lead patch management and vulnerability remediation programs ensuring compliance and system integrity 

• Develop and implement automation solutions using modern approaches including Vibe Coding (AI-assisted development) to accelerate operational efficiency and reduce toil 

• Support and optimize infrastructure for AI/ML workloads, including provisioning, scaling, and performance tuning 

• Manage and maintain GPU-enabled environments and instances for high-performance computing and machine learning use cases

• Oversee and optimize infrastructure monitoring, logging, alerting, and observability frameworks 

• Manage and mentor a team of systems engineers; provide technical guidance and performance oversight 

• Collaborate with architecture, security, and development teams to improve reliability, scalability, and operational efficiency 

• Support hybrid environments including cloud platforms and on-premise data centers 

• Ensure proper documentation, runbooks, SOPs, and operational readiness

• Stay abreast of new technologies in your areas but not limited to US Federal Standards, NIST Publications, cloud computing & deployment, site reliability engineering, security standards and compliance best practices etc. 

Requirements:

• Must have 5+ years of experience leading operations team with hands-on experience in driving operational  process improvements and technological advancements.

• Proven experience implementing and operating within ITIL frameworks 

• Must have 10+ years of hands-on Unix/Linux experience that includes specific technical experience with CentOS / Red Hat systems administration support for large scale distributed environments

• Hands-on experience with incident management, patching, system hardening, and production support 

• Experience building and maintaining golden images and standardized environments 

• Strong scripting/automation skills (e.g., Python, Bash, PowerShell or similar) 

• Experience with configuration management and automation tools (Ansible, Terraform, Puppet, Chef, or similar) 

• Strong understanding of networking fundamentals (DNS, TCP/IP, firewalls, load balancing) 

• Experience with monitoring and logging tools (e.g., Nagios, Splunk, ELK, Prometheus, Grafana)

• Must have Cloud Build-Out or Migration experience in at least one of the following providers Amazon AWS, Google GCP and Microsoft Azure

• Must have 2+ years with CI/CD and automation tools such as Terraform, Ansible, Chef, Puppet, Jenkins, GitHub

• Experience supporting AI/ML workloads or data-intensive platforms 

• Familiarity with GPU-based compute environments (e.g., NVIDIA GPU instances)

• Must be willing to learn new technologies, adopt and adapt to emerging technologies or needs from a project to a project

• Knowledge of security best practices and compliance frameworks such as NIST 800-53, FedRAMP, FISMA etc. are preferred

• Certifications such as ITIL, Linux, AWS, Azure, or Kubernetes (CKA/CKAD) is preferred

• Networking certifications (CCNA/CCNP) are a plus

Position Overview

Axle is hiring a mid-level PowerApps / SharePoint Developer to support the National Institutes of Health (NIH) in improving collaboration, automating workflows, and streamlining business processes. The ideal candidate will have strong hands-on experience building PowerApps solutions, configuring SharePoint Online sites, and integrating data from multiple sources to support research and administrative programs across NIH. This is an excellent role for someone looking to contribute directly to NIH’s mission while growing technical skills in the Microsoft Power Platform and federal IT environment.

Key Responsibilities

• Develop and maintain PowerApps (Canvas and Model-Driven) applications that automate NIH business processes and enhance data accessibility. 

• Design and configure SharePoint Online sites, lists, pages, and web parts to improve collaboration and document management. 

• Create Power Automate workflows to streamline approvals, notifications, and data movement. 

• Integrate Power Platform solutions with other Microsoft 365 tools (Teams, Outlook, Excel) and external data sources (SQL Server, SharePoint). 

• Support troubleshooting, enhancements, and updates for existing PowerApps and SharePoint applications. 

• Ensure app designs meet Section 508 accessibility and agency security guidelines (HHS IT standards). 

• Work closely with business users and NIH staff to gather requirements, provide user support, and deliver clear documentation. 

• Participate in testing, code reviews, and version control to maintain quality and consistency across solutions. 

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or a related field. 

• 3–5 years of professional experience developing with PowerApps and SharePoint Online. 

• Proficiency in PowerApps formulas, Power Automate flows, and SharePoint configuration. 

• Solid understanding of data modeling, connectors, and integration with Microsoft 365 services. 

• Experience using basic scripting or APIs (PowerShell, REST, or Microsoft Graph). 

• Strong attention to detail, problem-solving ability, and communication skills. 

• Ability to work onsite in Bethesda, MD 2 days a week. 
• Ability to obtain and maintain a Public Trust clearance. 

Preferred Qualifications

• Experience supporting HHS, NIH, or another federal health agency. 

• Familiarity with Azure Active Directory, Dataverse, or SQL Server. 

• Microsoft Certified: Power Platform App Maker Associate or similar certification. 

• Basic understanding of HTML, CSS, or JavaScript for SharePoint UI customization. 

Why Join Us

At Axle, you’ll be part of a mission-driven team helping NIH harness technology to advance biomedical research and operational excellence. You’ll enjoy a collaborative, learning-focused environment with opportunities to expand your technical expertise in the Microsoft Power Platform ecosystem.

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

Axle is seeking a Scientific Software Engineer that will develop the integration middleware and interactive interfaces for the SOM Center. This position involves building cloud-native applications that establish interoperability between cloud-based AI services, legacy laboratory systems, and real-time experimental state systems, delivering a unified data management interface for scientific discovery data.

Responsibilities

• API Development: Design and implement high-concurrency asynchronous APIs (using GraphQL or gRPC) to stream data between cloud inference endpoints (e.g., Vertex AI) and centralized state stores.
• AI-Assisted Development: Actively utilize AI-assisted coding tools (e.g., GitHub Copilot) to accelerate feature implementation, unit testing, and debugging.
• User Portal & Visualization: Develop the SOM User Portal using modern frameworks to render interactive visualizations. Implement WebGL-based components (e.g., Deck.gl, PixiJS) to render high-density parallel protocol topologies and multi-omics heatmaps without performance degradation.
• Agentic AI Integration: Implement the application logic to support Agent-based workflows, connecting user-facing chat interfaces with backend reasoning engines. Handle complex interaction patterns such as tool execution loops, multi-step reasoning chains, and streaming responses to the user.
• LIMS Integration & Ontology Enforcement: Develop business logic to integrate modern microservices with Legacy Laboratory Information Management Systems (LIMS). Enforce Ontology Mapping at the user interface level to ensure all data entry adheres to the SOM controlled vocabulary.
• Real-Time State Store: Implement a high-frequency experimental state store using in-memory databases (e.g., Redis), utilizing WebSockets or Server-Sent Events (SSE) to power real-time dashboards that reflect experimental data states.
• Cloud-Native Patterns: Implement Cloud-Native design patterns to reliably consume outputs from asynchronous cloud operations and surface results to the user.
• Collaboration: Collaborate with AI researchers to create explainable interfaces for model predictions.

Required Qualifications

• Bachelor’s or master’s degree in computer science or software engineering with experience in full-stack development for data-intensive applications.
• Proficiency in modern Python backend frameworks (FastAPI, Litestar) and strongly typed frontend ecosystems (React/Next.js with TypeScript).
• Experience with WebGL-based visualization libraries (e.g., Deck.gl, PixiJS, or Plotly) for rendering high-density biological datasets.
• AI Productivity: Demonstrated ability to leverage AI-driven coding assistants and LLMs to increase development velocity and code quality.
• Experience deploying and debugging applications in Containerized Cloud Environments (Cloud Run, ECS, or Kubernetes).
• Preferred Qualifications
• Familiarity with Agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen) and implementing "Tool Use" patterns in applications.
• Experience with Event-Driven Architectures (Pub/Sub, Kafka) for coordinating hybrid workflows.
• Knowledge of Graph Database query languages (Cypher/Gremlin) and Real-Time/In-Memory databases (Redis).
• Familiarity with integrating applications with Enterprise Identity Providers (OIDC/SAML) and experience working in a biotech or LIMS environment.

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.