Tech Demand

What is worth learning · reported week 2026-W37 (last completed ISO week, SGT) · 0-2 yrs · AI-ML
Experience: all0-23-56+unstated
Role: allAI-MLBackendDataFrontendFullstackMobileOther-ITPlatformSRESecurity

1. Demand ranking

14 enriched SWE postings in 2026-W37. Share = postings mentioning the technology ÷ that number — postings still awaiting enrichment are excluded from the denominator, so a processing backlog cannot depress every share at once. The chart shows the top 15; the table in section 3 lists the top 30.

machine-learning12python12llm6cpp5rag5aws4azure4google-cloud4computer-vision3generative-ai3java3pytorch3tensorflow3docker2linux2

2. Momentum (vs the previous 4 weeks)

Heating up

TechnologyShareChangePostings
machine-learning85.7%+31.9pp12
python85.7%+3.8pp12

Cooling down

Nothing fell this week.

Change is in percentage points of share, not relative percent: a technology going from 1 to 3 postings would otherwise read as +200% and top the board. Boards consider every technology above the bar, not only the 30 the table below shows.

3. Salary premium and entry-friendliness

Premium compares the median advertised monthly salary of postings mentioning a technology against the overall median, over the trailing 90 days. Baseline: S$6750, the median of 185 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (185 of 185, in any unit); the rest hide it, and no figure here describes them. Entry-friendly is computed over the same 90-day window. Premium mixes seniority in (senior roles name more infrastructure); pick an experience band above to compare within one. Entry-friendly = the share of postings mentioning the technology that ask for at most 2 years' experience, or are Intern/Junior roles with no stated requirement. The table lists the top 30 technologies by postings.

TechnologyKindPostingsShareSalary premiumEntry-friendly
machine-learningai1285.7% +11.1% 100.0%
pythonlanguage1285.7% +3.7% 100.0%
llmai642.9% -11.1% 100.0%
cpplanguage535.7% +11.1% 100.0%
ragai535.7% -11.1% 100.0%
awscloud428.6% -14.8% 100.0%
azurecloud428.6% -15.6% 100.0%
google-cloudcloud428.6% -18.5% 100.0%
computer-visionai321.4% +0.0% 100.0%
generative-aiai321.4% -11.1% 100.0%
javalanguage321.4% +11.1% 100.0%
pytorchai321.4% +3.7% 100.0%
tensorflowai321.4% +0.0% 100.0%
dockertool214.3% +0.0% 100.0%
linuxtool214.3% +44.4% 100.0%
mongodbdatabase214.3% —(n=4) 100.0%
nlpai214.3% +44.4% 100.0%
openaiai214.3% —(n=14) 100.0%
redisdatabase214.3% —(n=6) 100.0%
csslanguage17.1% —(n=2) 100.0%
golanguage17.1% —(n=12) 100.0%
htmllanguage17.1% —(n=3) 100.0%
javascriptlanguage17.1% —(n=9) 100.0%
kotlinlanguage17.1% —(n=1) 100.0%
kubernetestool17.1% +3.7% 100.0%
langchainai17.1% -29.6% 100.0%
nodejslanguage17.1% —(n=7) 100.0%
phplanguage17.1% —(n=1) 100.0%
rubylanguage17.1% —(n=1) 100.0%
shelllanguage17.1% —(n=5) 100.0%

4. What else they ask for

These are MyCareersFuture's own skill tags — the competencies the employer filled in on the form, over the trailing 90 days (2026-06-23 → 2026-09-20) across 185 postings. They are not the technology ranking above: languages and frameworks appear only in the free-text description, which is why this system reads it separately. "Must-have" is the share of postings listing the tag that marked it essential rather than desirable — a tag that is everywhere but rarely essential is table stakes, one that is usually essential is a filter someone is applying.

SkillPostingsShareMarked must-have
Machine Learning7037.8%7.1%
PyTorch5328.6%11.3%
Artificial Intelligence5127.6%5.9%
Python5127.6%3.9%
Computer Science4825.9%6.2%
TensorFlow4122.2%0.0%
Data Science3720.0%5.4%
Natural Language Processing3619.5%8.3%
C++2614.1%3.8%
Computer Vision2614.1%26.9%
Pipelines2614.1%3.8%
Linux2413.0%16.7%
Data Pipeline2111.4%9.5%
Design2111.4%0.0%
LLMs2111.4%9.5%
Numbers computed by SQL from public MyCareersFuture data; data is refreshed daily, so it lags the live market by up to 24h. Methodology: docs/03-data-model.md · data freshness · Compliance: aggregate statistics only, no personal data.