57 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.
| Technology | Share | Change | Postings |
|---|---|---|---|
| machine-learning | 73.7% | +15.8pp | 42 |
| generative-ai | 38.6% | +11.3pp | 22 |
| aws | 36.8% | +10.7pp | 21 |
| fastapi | 17.5% | +8.1pp | 10 |
| docker | 40.4% | +6.3pp | 23 |
| google-cloud | 28.1% | +5.3pp | 16 |
| rag | 42.1% | +4.6pp | 24 |
| javascript | 19.3% | +4.5pp | 11 |
| java | 17.5% | +4.2pp | 10 |
| kubernetes | 28.1% | +3.9pp | 16 |
| Technology | Share | Change | Postings |
|---|---|---|---|
| pytorch | 17.5% | -24.5pp | 10 |
| python | 64.9% | -15.1pp | 37 |
| llm | 50.9% | -2.9pp | 29 |
| sql | 17.5% | -2.9pp | 10 |
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.
Premium compares the median advertised monthly salary of postings mentioning a technology against the overall median, over the trailing 90 days. Baseline: S$8500, the median of 615 postings advertising a monthly range — medians pin the unit so the figures are comparable. Separately, 100.0% of SWE postings state pay at all (615 of 615, 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.
| Technology | Kind | Postings | Share | Salary premium | Entry-friendly |
|---|---|---|---|---|---|
| machine-learning | ai | 42 | 73.7% | +5.9% | 31.3% |
| python | language | 37 | 64.9% | -5.9% | 31.0% |
| llm | ai | 29 | 50.9% | +0.0% | 22.7% |
| rag | ai | 24 | 42.1% | -5.9% | 21.1% |
| docker | tool | 23 | 40.4% | -14.7% | 16.7% |
| generative-ai | ai | 22 | 38.6% | +5.9% | 19.9% |
| aws | cloud | 21 | 36.8% | +2.9% | 22.6% |
| azure | cloud | 19 | 33.3% | -5.9% | 17.9% |
| google-cloud | cloud | 16 | 28.1% | -5.9% | 22.0% |
| kubernetes | tool | 16 | 28.1% | +2.9% | 16.9% |
| openai | ai | 12 | 21.1% | -5.9% | 13.0% |
| javascript | language | 11 | 19.3% | -14.7% | 11.2% |
| fastapi | framework | 10 | 17.5% | -8.8% | 18.5% |
| java | language | 10 | 17.5% | +14.7% | 32.9% |
| pytorch | ai | 10 | 17.5% | -5.9% | 29.9% |
| sql | language | 10 | 17.5% | -8.8% | 19.4% |
| deep-learning | ai | 8 | 14.0% | -11.8% | 35.1% |
| nodejs | language | 8 | 14.0% | -14.7% | 10.1% |
| snowflake | database | 8 | 14.0% | —(n=13) | 15.4% |
| tensorflow | ai | 8 | 14.0% | -11.8% | 32.8% |
| cpp | language | 7 | 12.3% | +17.6% | 50.5% |
| langchain | ai | 7 | 12.3% | -11.8% | 25.6% |
| react | framework | 7 | 12.3% | -14.7% | 20.0% |
| computer-vision | ai | 5 | 8.8% | -11.8% | 45.5% |
| nlp | ai | 5 | 8.8% | +17.6% | 42.7% |
| typescript | language | 5 | 8.8% | +8.8% | 22.9% |
| postgresql | database | 4 | 7.0% | +0.0% | 27.3% |
| redis | database | 4 | 7.0% | —(n=18) | 33.3% |
| terraform | tool | 4 | 7.0% | —(n=17) | 0.0% |
| airflow | tool | 3 | 5.3% | +29.4% | 36.4% |
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 615 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.
| Skill | Postings | Share | Marked must-have |
|---|---|---|---|
| Python | 186 | 30.2% | 7.0% |
| Computer Science | 163 | 26.5% | 4.3% |
| Machine Learning | 154 | 25.0% | 9.7% |
| Artificial Intelligence | 137 | 22.3% | 16.8% |
| PyTorch | 131 | 21.3% | 6.1% |
| AI Agents | 109 | 17.7% | 6.4% |
| TensorFlow | 107 | 17.4% | 0.9% |
| Ai | 89 | 14.5% | 10.1% |
| Design | 85 | 13.8% | 0.0% |
| Data Science | 83 | 13.5% | 6.0% |
| LLMs | 81 | 13.2% | 8.6% |
| C++ | 65 | 10.6% | 4.6% |
| Natural Language Processing | 63 | 10.2% | 7.9% |
| Linux | 58 | 9.4% | 6.9% |
| Data Pipeline | 57 | 9.3% | 5.3% |