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.

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.