Company details

HEADCOUNT

1-24

ORG TYPE

Startup

SECTOR

Technology & Digital

WEBSITE

http://www.dunia.ai/

About the company

Dunia Innovations is a Berlin-based deep tech company building an AI-native materials acceleration platform, combining physics-informed AI with lab automation and robotics to speed up materials discovery for clean energy use-cases (electrocatalysts for green hydrogen and CO₂ conversion are a stated focus). Public profiles describe a benefits package that includes mentorship, stock options, and hybrid working, alongside a “design your own perks” approach. Funding coverage includes a $11.5m raise reported in October 2024, and a company profile summarising total funding at $14.5m across two rounds.

Locations and presence

Dunia Innovations is headquartered in Berlin, with the office listed in Adlershof. Recent hiring pages also show visa process support for non-EU candidates, which points to an internationally open talent approach even with a Germany-centred footprint.

Palpable Score

59.8

/ 100

Dunia Innovations shows credible early-career entry points through working-student and internship roles tied to core engineering and ML work, plus lab technician hiring that can suit earlier-career profiles. The score is held back by limited pay transparency and a lack of public early-career outcomes evidence like conversion, progression, or review-platform signals.

Pillar 1: Early-career access

Score

15.0

/ 20

The company has offered a Working Student - Mechanical Engineering (f/m/d) role that sits inside hardware engineering and prototyping work rather than generic admin.
Dunia Innovations has also listed a Machine Learning Intern (f/m/d) - Materials Informatics, giving a direct entry route into the platform side of the company.
The company has advertised Lab technician (f/m/d) - Multiple openings, which is a practical early-career doorway for candidates with hands-on lab skills.

Pillar 2: Hiring fairness and transparency

Score

13.7

/ 20

The company’s working-student posting spells out concrete tasks like CAD support, simulations, testing, and technical documentation, which makes role scope legible before applying.
Dunia Innovations routes applications through an ATS-style flow (Personio) with a fallback contact email for upload issues, which is more structured than email-only hiring.
The company does not consistently publish interview stages, timelines, or assessment expectations on the postings surfaced via job boards, so candidates have to guess how selection works.

Pillar 3: Learning and support

Score

12.7

/ 20

The company publicly lists “mentorship to help you grow into the roles you want” as a recurring benefit across multiple roles, which is a direct early-career support signal.
Dunia Innovations positions early-career roles inside cross-functional build work, for example instrumentation integration into the materials acceleration platform in the working-student posting.
The company does not describe onboarding, 1:1 cadence, training budget, or progression expectations in a way an early-career candidate can verify before joining.

Pillar 4: Pay fairness and stability

Score

11.7

/ 20

The company lists “competitive salary & stock options” for lab technician hiring, but does not publish ranges, which caps pay fairness for early-career applicants comparing offers.
Dunia Innovations includes stability signals like 30 days paid holiday, hybrid working, and visa-process support in benefits lists that appear repeatedly across postings.
The company has no clearly attributable public salary benchmarks on major review platforms for this specific employer name, so compensation competitiveness cannot be independently checked from public data.

Pillar 5: Early-career outcomes

Score

6.7

/ 20

The company does not have reliable, clearly attributable review-platform coverage under “Dunia Innovations” that speaks to junior retention, manager quality, or promotion experiences.
Dunia Innovations has multiple early-career-friendly postings appearing within the same period (working student, ML intern, lab technician), but public pages do not show conversion outcomes or internal progression examples.
The company’s public LinkedIn headcount band gives a rough size signal, but it is not enough to track 12–24 month early-career progression patterns without tenure and role-change evidence.