Company details

HEADCOUNT

1-24

ORG TYPE

Startup

SECTOR

Technology & Digital

WEBSITE

https://www.phoebe.ai

About the company

Phoebe builds AI agents for software reliability, positioned as an “immune system for software” that investigates anomalies and alerts, then helps generate fixes and preventative changes. The company publicly launched in August 2025 alongside a $17M seed round led by GV and Cherry Ventures. Public materials describe the product working across siloed data like logs, traces, commits, and infrastructure signals, with early-access customers including Trainline and PPRO. Phoebe was founded in 2024 by Matt Henderson and James Summerfield (former Stripe Europe leaders).

Locations and presence

Phoebe is described publicly as London-based, with roles advertised as London hybrid with remote flexibility. The company’s customer references and positioning suggest a UK and Europe footprint, but public hiring content is still concentrated around London.

Palpable Score

40.7

/ 100

Phoebe looks like a strong technical startup, but public hiring signals currently skew toward experienced hires rather than early-career entry points. Hiring content is reasonably structured for a small team, yet pay transparency, onboarding details, and early-career outcomes are mostly missing, which limits the score.

Pillar 1: Early-career access

Score

4.3

/ 20

The company’s publicly visible hiring is narrow right now, with a single Engineering opening framed for a seasoned ML Engineer rather than a 0–3 year candidate.
Phoebe’s ML Engineer posting reads as “built ML systems at scale” and “LLM practitioner” level expectations, which closes off most graduate applicants.
The company does not show recurring junior titles (Junior, Associate, Analyst, Apprentice) or intern-to-full-time pathways in current public listings, so early-career access is limited.

Pillar 2: Hiring fairness and transparency

Score

12.0

/ 20

The company uses a formal ATS flow (Ashby) rather than ad-hoc email hiring, which usually improves consistency for applicants.
Phoebe’s ML Engineer listing is unusually specific about scope, including agentic workflows, multi-modal analysis across observability data, and evaluation infrastructure, which helps candidates self-select.
The company does not publish the interview stages, timelines, or what “success” looks like across steps, so transparency stops at the job-description level.

Pillar 3: Learning and support

Score

7.7

/ 20

The company frames the ML Engineer role as early-team, high-ownership work across architecture, production deployment, and evaluation, which can create fast learning through breadth.
Phoebe’s public materials focus on product capability and research-style claims, but role text does not spell out coaching mechanisms like pairing, 1:1 cadence, or a ramp plan.
The company does not publicly signal structured early-career support such as internships, a buddy system, or progression frameworks, which caps this pillar for graduates.

Pillar 4: Pay fairness and stability

Score

9.7

/ 20

The company explicitly references “competitive salary” plus equity for an early employee in the ML Engineer listing, which is a positive baseline signal.
Phoebe does not publish salary ranges in public job ads, which makes it hard for early-career candidates to judge fairness before investing time.
The company’s public job materials also do not spell out benefits coverage (healthcare, pension, leave) for UK hires, so stability signals are incomplete.

Pillar 5: Early-career outcomes

Score

7.0

/ 20

The company has clear external traction signals through named early-access customers (Trainline and PPRO) and a public product launch narrative, which suggests meaningful work exposure for early hires.
Phoebe raised a $17M seed round in August 2025, which improves runway stability, but funding alone does not show junior retention or promotions.
The company’s LinkedIn footprint shows a small team size, but public evidence is missing on early-career progression, conversion rates from internships, and 12–24 month retention patterns.