Top Metros for Postsecondary Computer Science Teachers in 2026: New York Leads, High Salaries Cluster in California and Kansas

Across the top 10 metros for postsecondary computer science teachers, LaborCove’s data highlights distinct patterns rather than a single “best” profile. New York-Newark-Jersey City, NY-NJ leads the rankings with the largest reported local employment count and highest Geography Score, even though its median salary is below the national median. At the same time, several smaller markets in employment terms post substantially higher median salaries than both New York and the national figure, including San Diego-Chula Vista-Carlsbad, CA; Santa Maria-Santa Barbara, CA; and Lawrence, KS. The results show that higher pay does not automatically translate into the top Geography Score or highest rank, and larger employment counts do not always coincide with the highest salaries.

For postsecondary computer science teachers as a national occupation, LaborCove reports a median annual salary of $96,980 in 2026. National employment stands at 35,480, providing the backdrop against which metro-level employment counts can be viewed. The national projected employment growth rate for this occupation is 5.3%. This projection applies to the occupation across the United States and is not a state- or metro-specific forecast. In the sections that follow, this national benchmark helps frame how the top 10 metros compare on salary and employment, without implying any local growth rate.

New York-Newark-Jersey City, NY-NJ holds the #1 rank in LaborCove’s 2026 metro results for postsecondary computer science teachers, with a Geography Score of 65.50. Its reported employment count of 2,830 is the largest among the top geographies supplied and matches the stated largest employment market figure of 2,830. New York’s median annual salary of $93,980 is slightly below the national median of $96,980. This combination—top rank and Geography Score, largest supplied employment count, and a median salary that trails the national figure—illustrates that the LaborCove ranking is not driven solely by pay levels.

Several top-ranked metros combine high LaborCove Geography Scores with median salaries that exceed the national median of $96,980. San Diego-Chula Vista-Carlsbad, CA, ranked #2 with a Geography Score of 54.80, has a median salary of $166,550, which is higher than both New York’s $93,980 and the national benchmark. Its reported employment count is 300. Santa Maria-Santa Barbara, CA, ranked #3 with a Geography Score of 52.45, reports a median salary of $173,640 and employment of 70. Lawrence, KS, ranked #5 with a Geography Score of 50.15, has a median salary of $170,660 and employment of 40. Each of these metros shows substantially higher median salaries than the national figure but smaller reported employment counts than New York’s 2,830. Boston-Cambridge-Newton, MA-NH also appears near the top of the rankings at #4, with a Geography Score of 50.39. Its median salary of $119,810 is above the national median, and its employment count of 1,060 is larger than the employment counts reported for San Diego, Santa Maria-Santa Barbara, Lawrence, San Jose-Sunnyvale-Santa Clara, San Francisco-Oakland-Fremont, Los Angeles-Long Beach-Anaheim, and Salt Lake City-Murray within the top 10 list.

Four California metros appear in the top 10: San Diego-Chula Vista-Carlsbad (#2), Santa Maria-Santa Barbara (#3), San Jose-Sunnyvale-Santa Clara (#6), and San Francisco-Oakland-Fremont (#7). All four report median salaries above the national median of $96,980. San Diego’s median salary of $166,550 and Santa Maria-Santa Barbara’s $173,640 are both higher than San Jose-Sunnyvale-Santa Clara’s $125,720 and San Francisco-Oakland-Fremont’s $133,330. However, San Jose and San Francisco have higher reported employment counts—340 and 220, respectively—than Santa Maria-Santa Barbara’s 70 and Lawrence, KS’s 40, while still remaining below New York’s 2,830 and Boston’s 1,060. Despite their similar state context, these California metros show different balances among salary, employment counts, and Geography Scores: San Diego ranks #2 with a Geography Score of 54.80, Santa Maria-Santa Barbara ranks #3 at 52.45, San Jose-Sunnyvale-Santa Clara ranks #6 at 48.42, and San Francisco-Oakland-Fremont ranks #7 at 47.18.

Bridgeport-Stamford-Danbury, CT holds the #8 rank with a LaborCove Geography Score of 45.44 and a median salary of $100,100, which is higher than the national median of $96,980. Employment data for Bridgeport-Stamford-Danbury are not supplied, so only its salary and score can be compared. Los Angeles-Long Beach-Anaheim, CA ranks #9 with a Geography Score of 45.27, a median salary of $109,930, and employment of 820. Its median salary is above both the national median and New York’s $93,980, and its employment count is higher than those reported for San Diego, Santa Maria-Santa Barbara, Lawrence, San Jose-Sunnyvale-Santa Clara, San Francisco-Oakland-Fremont, and Salt Lake City-Murray. Salt Lake City-Murray, UT rounds out the top 10 with a Geography Score of 44.69, a median salary of $135,790, and employment of 240. Its median salary is above the national benchmark and higher than New York’s $93,980, while its reported employment count is lower than New York’s 2,830, Boston’s 1,060, and Los Angeles’s 820 but higher than Santa Maria-Santa Barbara’s 70 and Lawrence’s 40.

LaborCove’s rankings for postsecondary computer science teachers rely on authoritative labor-market data, including national and metro-level figures. For each metro, we use the supplied median annual salary, employment count where available, and LaborCove Geography Score and rank. National occupational data provide context on overall salary levels, employment, and projected national employment growth for the occupation. The LaborCove Geography Score aggregates these and potentially other factors into a single comparable value for each area, which is then used to produce the ranking. The specific contribution of salary, employment, or other components to the Geography Score is not disclosed in the evidence, so this article interprets only the visible numeric differences. Career decisions should weigh these rankings alongside individual priorities such as personal circumstances and institutional fit. LaborCove’s results highlight quantifiable differences across locations, but they do not estimate hiring probability or guarantee outcomes for any individual.