Ameba

Autonomous AI agents platform
Last updated:
January 28, 2026
Company details
HQ
HEADCOUNT
1-24
ORG TYPE
Startup
SECTOR
Technology & Digital
About the company
Ameba builds an AI platform for supply chain teams, focused on turning messy operational data from emails, spreadsheets, PDFs, and messaging apps into real-time visibility and alerts. The company positions the product as “no systems change”, meaning teams keep their existing tools while Ameba sits on top to extract and structure information. Ameba’s product messaging is especially aimed at fashion, garments, and consumer brands, where supplier communication is high-volume and fragmented. Ameba publicly announced a $7.1M seed round in September 2024 and describes founders with supply chain operator and Palantir engineering backgrounds.
Locations and presence
Ameba is London-based, with an office address in Spitalfields/Commercial Street shown on the website and job listings. Public hiring materials also describe fully in-office working for at least one engineering role.
Palpable Score
44.0
/ 100
Ameba looks like a high-intensity, founder-close environment with clear role expectations and unusually strong pay transparency for at least one engineering role. For graduates, the biggest issue is access: the visible roles are senior-leaning, and there is almost no public evidence on early-career progression, mentoring, or junior retention.
Pillar 1: Early-career access

Score

4.0
/ 20
  • The company’s publicly visible engineering role is “Founding Software Engineer” and asks for at least 3 years of experience, which blocks most entry-level applicants.
  • Ameba lists “Mid, Senior and Expert level” for the same role rather than junior or graduate-level hiring.
  • The company does not show internships, apprenticeships, or 0–2 year roles on the main company careers surface, and the company page currently states there are no open roles.
Pillar 2: Hiring fairness and transparency

Score

14.0
/ 20
  • The company publishes an application flow for at least one role: behavioural video interview, technical remote interview, and onsite.
  • Ameba publicly states five days a week in-office for a core engineering role, which is a clear expectation rather than vague “hybrid” wording.
  • The company does not publicly time-box any assessments or publish a consistent stage-by-stage process across an official careers page, so candidates are reliant on third-party listings for process clarity.
Pillar 3: Learning and support

Score

10.5
/ 20
  • The company positions a core engineering role as working closely with the CTO across front-end, back-end, and infrastructure, which can create steep learning through proximity to senior decision-makers.
  • Ameba frames delivery as fast feedback loops with users, which can build practical product sense quickly for early-career hires who already have solid basics.
  • The company does not publish onboarding plans, mentoring expectations, or review cadence, so a new grad cannot validate support quality before joining.
Pillar 4: Pay fairness and stability

Score

11.5
/ 20
  • The company shares a salary figure and equity range publicly for a key engineering role, which is better pay transparency than most startups at this size.
  • Ameba’s published compensation is attached to a senior role, so it does not answer the question of junior pay fairness or how compensation scales down for earlier levels.
  • The company does not publicly list benefits (healthcare, pension, leave policies) in a consistent, official way, which limits how well early-career candidates can assess stability.
Pillar 5: Early-career outcomes

Score

4.0
/ 20
  • The company has no clearly attributable public employee review footprint for Ameba Systems Ltd., which removes a major validation source on manager quality and early-career experience.
  • Ameba does not publish promotion stories, leveling, intern conversion, or “career paths” content, so progression outcomes are not verifiable.
  • The company’s LinkedIn profile shows a very small team size band, and there is not enough public tenure and progression patterning to score early-career outcomes with confidence.
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