Bildungsgutschein Job Placement Rates 2026: Provider Outcomes Compared
13 min · October 7, 2026
Key Takeaways
Official benchmark: roughly half (48 to 49%) of comparable jobseekers who did not train were in work a year later, according to IAB research on 2023–24 course starts.
Provider claims of 85 to 94% are rarely published with a method behind them. Traced to source, the only other figure aimed at funded learners is neue fische's: more than 60% in a new job within six months.
The gap comes from who is counted: graduates or all starters, any job or a job in the field, and how many unreachable learners are dropped.
The time window changes everything. A placement rate without a stated time since course end tells you very little.
No public league table ranks providers by verified outcomes. Ask each provider the four questions below.
Worked example: Turing College's latest audit found 88% of funded learners (31 of 35) employed six or more months after programme end, with none excluded as unreachable. On Lithuania's public UŽT dashboard, 97% of its 1,400+ graduates were in work six months after training, upskillers included.
Some graduates build companies instead. Early Turing College learner Martynas Krupskis founded Fieldy, which passed $1.5M in sales.
Recent official research puts the baseline at roughly half: the IAB found that 48 to 49% of comparable jobseekers who did not take part in job-related training were in work a year later. Private providers are often credited with 85 to 94% in marketing roundups, but when we traced those claims to source, none was both specific to Bildungsgutschein learners and independently verified.
This guide compares the published job placement figures for Bildungsgutschein providers, explains why they differ so much, and gives four questions that expose a weak statistic. As a worked example, it includes one provider's full outcomes dataset, weaker figures included. We will update it every six months at this address.
Disclosure: this guide is written by Turing College, an AZAV-certified provider with offices in Berlin and Vilnius. We hold our own figures to the same standard as everyone else's and publish the full method behind them. Turing College is not affiliated with Turing School of Software & Design in Denver.
Which Bildungsgutschein providers have the best job outcomes?
No independent source ranks Bildungsgutschein providers by verified job outcomes. The fairest benchmark is official German data, because it counts everyone the same way. The most recent, an IAB study published in 2026, found that around 48 to 49% of comparable jobseekers who did not take part in job-related training were employed a year later. We only include data from the last two years: the tech job market has changed too much for older figures to be useful.
Funded training is common. In 2025, around 81,000 people receiving basic income support started a funded training course and about 74,000 completed it, according to a government answer to the Bundestag.
We traced each provider's headline figure back to its original source and tested it against two questions: does it cover Bildungsgutschein learners specifically, and has anyone independent verified it? Only official statistics pass both.
Source | Published figure | Who is counted | Specific to funded learners? | Independently verified? |
|---|---|---|---|---|
~48 to 49% employed | Comparable jobseekers who did not train, 1 year after 2023–24 course starts | Yes | Yes, official research | |
Turing College | 88% employed | 35 Bildungsgutschein learners 6+ months after programme end, none excluded (audit updated Sept 2026) | Yes | Internal audit; full method published here |
More than 60% in a new job | Graduates, within 6 months; stated on its page for Jobcenter job coaches | Partly | No | |
90.8% employed | Graduates, 180 days, undated, no country split; its site now states it is ceasing operations | No | No | |
WBS Coding School | None found | No figure published on its own site | No | No |
Figures quoted in SEO roundups, such as 92% for WBS Coding School or over 90% for neue fische, did not match anything we could find on the providers' own sites, so we left them out. If a provider publishes verified figures for funded learners, we will add them.
These figures are not like-for-like. Ours includes learners who did not finish as well as completers, because we count everyone whose programme access ended; most graduate-only figures do not. Programmes, regions and learner backgrounds also differ. Use the table to ask better questions, not to rank schools.
Why do placement rates differ so much between providers?
Because providers measure different things. The same group of learners can produce a rate tens of percentage points apart depending on four choices.
Starters or graduates. Counting only learners who finished removes those least likely to find work.
Any job or a job in the field. Some reports count any employment; others count only roles linked to the training.
Unreachable learners. Excluding them assumes they resemble those who answered.
The time window. Rates at three, six and twelve months differ sharply. A rate measured at three months will look very different from one measured at six or twelve, even for the same learners.
Publishing the method closes most of these gaps, because anyone can see who is counted and who is excluded. Independent verification goes a step further. Reporting standards such as CIRR required schools to back each outcome with documentation and have it verified by a third party.
How to check any provider's placement claim
Ask these four questions before you choose. A provider with solid results answers them quickly; vague answers are a warning sign.
What share of everyone who started, not just finished, is employed?
Is that a job in the field they trained for?
At what point after the course is it measured, and how many people are in the sample?
How many learners were excluded as unreachable, and who verified the data?
Case study: what a full outcomes dataset looks like
Most providers publish one number. Below is what publishing everything looks like, using Turing College's data for Bildungsgutschein-funded learners: how we count, who is included and who is excluded. Other providers can use the same structure.
What Turing College's 88% figure measures
It measures how many funded learners are in work at least six months after their programme ended. It does not mean 88% were hired within six months. Some found work in month two; others in month nine. The figure is a snapshot of where everyone stands once six months have passed.
The precise statement is this: of the 35 Bildungsgutschein-funded learners whose programme access had ended six or more months before our latest audit (July 2026, updated 30 September 2026), 31 were employed and 4 were not: 88.6%. None were excluded as unreachable; every learner in the group has a confirmed status.
Sample: funded learners 6+ months after programme end | Learners |
|---|---|
In the group | 35 |
Employed | 31 |
Not employed | 4 |
Excluded as unreachable or declined | 0 |
Employment rate | 88.6% |
We checked each learner in that group through LinkedIn or direct outreach by Discord, email and phone. We will add the full breakdown by time window and programme in the next update.
Employment rate = employed ÷ (employed + not employed).
How the data was collected and counted
We track every learner funded through the Agentur für Arbeit or Jobcenter, not just those who completed. The figures on this page come from our July 2026 audit, updated on 30 September 2026.
Population: all Bildungsgutschein-funded learners in our Salesforce records, across AI Engineering and two earlier funded programmes, AI for Business and AI for Business Analytics.
Employed: the learner confirmed they have a job, or their LinkedIn profile shows a clear current role.
Not employed: the learner responded and confirmed they have not found work yet.
Unknown: we could not reach the learner, or they declined to share. These learners are excluded from the rate and listed separately.
Outreach: where LinkedIn did not settle it, we contacted learners in sequence through Discord, email, LinkedIn message and up to three phone calls.
Time window: measured from the date the learner's programme access ended. Learners whose access ended early, for example because they started a job, are placed in the most recent group.
How to read these figures
We apply the four questions above to our own data.
Who is counted: every Bildungsgutschein-funded learner whose programme access ended six or more months before the audit, whether they completed or left early.
What counts as employed: in work at the time of the check, confirmed by the learner or shown in a current LinkedIn role.
Who is excluded: only learners we could not reach or who declined to share.
Time window: at least six months after the programme, the standard window most providers use.
Growing sample: funded programmes began in 2025, so each six-monthly update adds new cohorts to the dataset.
Roles and employers
Many funded graduates move into hands-on AI and data roles. Titles recorded in our 2026 audit for jobs that started after the programme include Full Stack AI Engineer, Member of Technical Staff and Data & Analytics Engineer.
One AI Engineering graduate, funded through the Bildungsgutschein, joined Eurowings Digital (Lufthansa Group) as a Staff Engineer working on AI initiatives. Others stayed with their employer and took on new AI responsibilities, which we record separately from new hires.
Across all Turing College programmes, not only funded ones, graduates have been hired by companies including Wise, Swedbank, Danske Bank, Vinted, Western Union, Tesla and Nasdaq.
Track record with Lithuania's Employment Service (UŽT)
Turing College has also delivered state-funded training in Lithuania through the Employment Service (Užimtumo tarnyba, UŽT). Unlike most provider claims, UŽT's results are public: its vocational training effectiveness dashboard reports outcomes for every funded provider, public and private, and updates quarterly.
In the July 2025 edition of the dashboard, more than 1,400 people had completed Turing College's UŽT-funded programmes in the previous 24 months.
Indicator (UŽT dashboard, July 2025) | Turing College |
|---|---|
Completed programmes in 24 months | 1,400+ |
In work six months after training | 97% |
Average monthly pay, 12 months before training | €3,107 |
Average monthly pay, 6 months after training | €3,523 (+€416, 13%) |
Took up self-employment after training | 475 |
Graduate rating | 4.6/5 (232 responses) |
Source: UŽT vocational training effectiveness dashboard, July 2025, as summarised in our analysis.
UŽT's "in work" measure covers both new hires and learners who upskilled in their current job, so we report it separately from the Bildungsgutschein figures above. The salary figures reflect Turing College's selective admissions and English-language specialist programmes: learners start from a strong base and still add more than €400 a month.
Beyond getting hired: graduates who built products and companies
An employment rate cannot count the learners who chose to build something of their own. These stories sit outside the dataset above, and several come from programmes other than the funded ones.
Martynas Krupskis, Fieldy. An early Turing College learner, Martynas launched Fieldy, a wearable AI note-taker, with a demo video, a Stripe link and one tweet. Fieldy has passed $1.5M in sales, earns six figures in monthly revenue and employs a team of 10. Read his story.
Edvard Sivickij, condense.chat. A former aviation engineer, Edvard retrained with Turing College and worked as a data analyst at Kilo Health. He then took AI Engineering, building three multi-agent systems, and co-founded condense.chat, a startup that trains its own token-compression models to cut the cost of running AI. Read his story.
Mario Wangen, PULSE. After 20 years as a software developer, Mario joined AI Engineering on a Bildungsgutschein and built PULSE, an app that helps startups create branded social media content. Read his story.
Dragan, KlarAmt. Dragan's Bildungsgutschein application was rejected. He appealed, won, joined AI Engineering and built KlarAmt, a tool that helps others through the same process. Read how he appealed.
Rimvydas Samulionis, Ceiling Advisor. After 20 years in construction, Rimvydas is building an assistant that answers questions from technical product documents, with sources architects can check. Read his story.
Frequently asked questions
What is the average job placement rate after a Bildungsgutschein course?
We could not find a recent official six-month figure. The closest is IAB research from 2026: about half (48 to 49%) of comparable jobseekers who did not train were in work a year later. Results vary widely by field, region and course length.
Which Bildungsgutschein provider has the highest job placement rate?
No one can say reliably. There is no public ranking based on verified outcomes, and none of the provider claims we traced was both specific to Bildungsgutschein learners and independently verified. Compare providers with the four questions above instead.
Is a 90% job placement rate realistic?
Yes, when the group and time window are clearly defined, as with Turing College's 88% for funded learners six or more months after their programme. Claims without a stated denominator are harder to judge, so always ask who is counted and how many learners were excluded.
Can I choose any provider with my Bildungsgutschein?
You can choose any AZAV-certified course that matches the training goal on your voucher. You can search approved courses on KURSNET, the Bundesagentur's course database, and your caseworker must approve the choice.
What is Turing College's job placement rate for Bildungsgutschein learners?
In our latest audit (updated 30 September 2026), 31 of 35 Bildungsgutschein-funded learners whose programme ended six or more months earlier were employed: 88.6%.
Does Turing College's 88% mean learners get a job within six months?
No. It means 88% were in work by the time six or more months had passed. Some found jobs much sooner, some later.
Which Turing College programmes does the data cover?
Learners from AI Engineering, plus AI for Business and AI for Business Analytics, which were funded through the Bildungsgutschein at the time. Today the funded programmes are AI Engineering and Building with AI Agents. Building with AI Agents launched in 2026 and will be included once its learners reach six months after completion.
Can I join with a Bildungsgutschein if I am not a German citizen?
Yes, if you live in Germany, are registered at your address and, for non-EU citizens, hold a permit that allows you to work. The Agentur für Arbeit or Jobcenter decides each case individually.
Are Turing College's funded programmes taught in German?
No. All funded programmes are taught in English, including materials, mentor sessions and project reviews.
How often is this data updated?
Every six months, at this address. The next update is due in March 2027.
Sources
Turing College AfA Employment Tracker, Salesforce learner records, audit July 2026, updated 30 September 2026 (internal)
Update log
Date | Change |
|---|---|
6 October 2026 | First publication, data from July 2026 audit, updated 30 September 2026 |
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