Company details

HEADCOUNT

1-24

ORG TYPE

Startup

SECTOR

Technology & Digital

WEBSITE

https://www.spaitial.ai

About the company

SpAItial AI is building “Spatial Foundation Models” that turn a text prompt or a single image into an explorable, 3D-consistent world, marketed under the model name “Echo”. The company positions the work at the intersection of generative AI, graphics, and spatial computing, with use cases spanning gaming, AR/VR, robotics, and digital twinning. Public coverage says SpAItial AI emerged from stealth in 2025 with a $13m seed round led by Earlybird, with Speedinvest participating.

Locations and presence

SpAItial AI lists London as headquarters and also lists Munich as a location. Current hiring is shown as in-office across London and Munich for the two visible roles.

Palpable Score

30.0

/ 100

SpAItial AI looks like a high-bar research-heavy startup where most “entry-level” pathways still expect specialist capability (for example a PhD diffusion role, or a graphics role that still asks for shipped work). The score is mainly limited by missing early-career signals: no published pay bands, no junior onboarding detail, and almost no independently verifiable outcomes data yet.

Pillar 1: Early-career access

Score

6.0

/ 20

The company’s most accessible opening is Research Engineer – Graphics, which allows a Bachelor’s or Master’s degree as an alternative to “2+ years” of industry experience.
SpAItial AI’s other advertised role (Research Scientist – Diffusion) targets a PhD plus a strong publication record, which rules out most 0–3 year candidates.
The company only shows two open positions publicly, with no visible internships, graduate intake, or junior titles beyond LinkedIn’s “entry level” label.

Pillar 2: Hiring fairness and transparency

Score

8.5

/ 20

The company describes concrete, role-relevant responsibilities (rendering pipelines, synthetic dataset generation, large-scale diffusion training) that help candidates self-select.
SpAItial AI includes an equal-opportunity statement in the job descriptions, which is a basic but meaningful fairness signal.
The company does not publish interview stages, time estimates, or assessment workload on the role pages visible, so candidates can’t judge process burden upfront.

Pillar 3: Learning and support

Score

5.8

/ 20

The company hires for work that should produce steep learning (building datasets and pipelines that directly power frontier model training), but the postings don’t describe coaching or ramp time.
SpAItial AI’s roles emphasise cross-functional collaboration with research and engineering teams, which can create feedback loops, but it’s not framed as structured mentorship.
The company does not publish onboarding plans, review cadence, or a learning budget, which caps confidence for early-career support.

Pillar 4: Pay fairness and stability

Score

5.5

/ 20

The company does not publish salary ranges or equity terms for the two visible openings, which makes pay fairness hard to benchmark before interviews.
SpAItial AI lists the roles as full-time, but does not publicly lay out benefits (healthcare, pension, leave, equipment), so stability signals are incomplete.
The company has external funding coverage, which supports general stability, but there isn’t enough role-level compensation evidence to score higher.

Pillar 5: Early-career outcomes

Score

4.2

/ 20

The company has no meaningful public employee review base yet (Glassdoor shows “be the first to add a review”), so retention and manager quality can’t be validated externally.
SpAItial AI does not publish early-career outcome proof such as intern conversion, promotion stories, or example leveling over 12–24 months.
The company’s LinkedIn footprint shows a very small team and early-stage hiring volume, which means there’s limited observable evidence of repeat junior cohorts or progression patterns.