Johns Hopkins University Applied Physics Laboratory tech stack
Johns Hopkins University Applied Physics Laboratory builds mostly with Python, MATLAB, C++, Java. Its stack also includes Linux, GitLab, PyTorch, React.js. Python is named in 266 of the 397 job postings Johns Hopkins University Applied Physics Laboratory published in the last 12 months (67%). Counted by Alion from the job postings of Johns Hopkins University Applied Physics Laboratory, updated Oct 27, 2026.
What programming languages does Johns Hopkins University Applied Physics Laboratory use?
Python is named in 67% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by MATLAB (52%), C++ (49%), Java (11%).
Python
266 67%
MATLAB
208 52%
C++
193 49%
Java
43 11%
JavaScript
40 10%
Rust
30 8%
TypeScript
26 7%
SQL
24 6%
Show all (26)
What frameworks and libraries does Johns Hopkins University Applied Physics Laboratory use?
PyTorch is named in 5% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by React.js (5%), Vue.js (4%), Angular (3%).
PyTorch
18 5%
React.js
18 5%
Vue.js
14 4%
Angular
12 3%
TensorFlow
11 3%
NumPy
7 2%
ROS
7 2%
ROS2
6 2%
Show all (52)
And 12 more, each named in fewer postings.
What databases does Johns Hopkins University Applied Physics Laboratory use?
PostgreSQL is named in 2% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by MySQL (2%), CouchDB (1%), HBase (1%).
PostgreSQL
7 2%
MySQL
6 2%
CouchDB
4 1%
HBase
4 1%
MS SQL
4 1%
SQLite
3 1%
ElasticSearch
2 1%
GraphDB
1 <1%
Show all (10)
What cloud and platforms does Johns Hopkins University Applied Physics Laboratory use?
Linux is named in 19% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by GitLab (7%), Kubernetes (6%), Windows (5%).
GitLab
27 7%
Kubernetes
23 6%
AWS
15 4%
Azure
9 2%
GitLab CI
8 2%
Show all (40)
What developer tools does Johns Hopkins University Applied Physics Laboratory use?
Docker is named in 9% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by Simulink (8%), Cameo Systems Modeler (7%), AFSIM (7%).
Docker
37 9%
Git
25 6%
Ghidra
13 3%
SLURM
12 3%
Show all (77)
And 37 more, each named in fewer postings.
What AI and ML technologies does Johns Hopkins University Applied Physics Laboratory use?
LLM is named in 2% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by ChatGPT (<1%), GPT-4 (<1%), Llama (<1%).
Which engineering practices does Johns Hopkins University Applied Physics Laboratory ask for?
Machine Learning is named in 23% of job postings at Johns Hopkins University Applied Physics Laboratory, followed by AI Agents (10%), MBSE (8%), CI/CD (8%).
AI Agents
40 10%
CI/CD
31 8%
HPC
19 5%
Computer Vision
10 3%
Show all (67)
And 27 more, each named in fewer postings.
What does each Johns Hopkins University Applied Physics Laboratory team work with?
The technologies each team's postings require most often, with the number of postings that require them.
Industrial Engineering
102 postingsAnalytics
44 postingsHardware
42 postingsEarly Careers
34 postingsBackend
33 postingsAI/ML
29 postingsHow does your stack fit Johns Hopkins University Applied Physics Laboratory?
How is the Johns Hopkins University Applied Physics Laboratory stack changing?
Read from the dates of the postings themselves: a technology is new when the first posting that names it appeared in the last six months, rising or falling when its share of postings moved between the last two quarters.
How modern is the Johns Hopkins University Applied Physics Laboratory stack?
Johns Hopkins University Applied Physics Laboratory scores 79 out of 100 (Cutting-edge). The score averages how current each technology in the stack is and adds weight for AI adoption. AI technologies in the stack: 7; legacy ones: 0.
How common is the Johns Hopkins University Applied Physics Laboratory stack in its industry?
Share of the 797 companies in the same industries, whose stack Alion knows, that use each technology.
What sets Johns Hopkins University Applied Physics Laboratory apart
Companies with a stack like Johns Hopkins University Applied Physics Laboratory
Questions about the Johns Hopkins University Applied Physics Laboratory tech stack
What programming languages does Johns Hopkins University Applied Physics Laboratory use?
The languages named in Johns Hopkins University Applied Physics Laboratory job postings are Python (67%), MATLAB (52%), C++ (49%), Java (11%), JavaScript (10%), as a share of the 397 postings of the last 12 months.
Does Johns Hopkins University Applied Physics Laboratory use PyTorch?
Yes. PyTorch is named in 18 of 397 job postings (5%) and required in 18 of them.
What cloud and platforms does Johns Hopkins University Applied Physics Laboratory use?
The cloud and platforms named in Johns Hopkins University Applied Physics Laboratory job postings are Linux (75), GitLab (27), Kubernetes (23) (postings in brackets).
What skills do you need for a Industrial Engineering job at Johns Hopkins University Applied Physics Laboratory?
Industrial Engineering postings at Johns Hopkins University Applied Physics Laboratory most often require MATLAB (61), Python (57), C++ (47), Cameo Systems Modeler (11), out of 102 postings in the last 12 months.
Is the Johns Hopkins University Applied Physics Laboratory tech stack changing?
Yes. Tableau, Pydantic AI, AS9100, Platform Engineering first appeared in Johns Hopkins University Applied Physics Laboratory job postings in the last six months.
How modern is the Johns Hopkins University Applied Physics Laboratory tech stack?
Johns Hopkins University Applied Physics Laboratory scores 79 out of 100 (Cutting-edge). The score averages how current each technology in the stack is and adds weight for AI adoption. AI technologies in the stack: 7; legacy ones: 0.
How does Alion know the Johns Hopkins University Applied Physics Laboratory tech stack?
Alion reads every job posting Johns Hopkins University Applied Physics Laboratory publishes and tags the technologies each one names. The figures on this page count the 397 postings of the last 12 months and were updated on Oct 27, 2026.
Method and citation
Alion reads every job posting the company publishes and tags the technologies each one names. Shares count the postings of the last 12 months, open and closed; the company's own products are counted apart. How we count
Alion, "Johns Hopkins University Applied Physics Laboratory tech stack: 279 technologies from 397 job postings", as of Oct 27, 2026. https://alion.io/company/jhuapl/technologies