Murphy

AI debt recovery platform
Last updated:
January 27, 2026
Company details
HQ
HEADCOUNT
25-99
ORG TYPE
Startup
SECTOR
Finance
About the company
Murphy AI builds an AI-powered debt collection platform where automated agents handle outreach across channels like voice, email and SMS, aiming to improve recovery rates while keeping communication respectful and personalised. Murphy AI describes the product as replacing labour-heavy call-centre workflows with scalable automation for businesses collecting overdue invoices. Public funding coverage in 2025 places Murphy AI as a Barcelona-based startup backed by venture investors to scale the platform. Current hiring centres on engineering and deployment-facing roles.
Locations and presence
Murphy AI is headquartered in Barcelona, with job ads pointing to a hybrid setup from the Poblenou office. The public careers site currently lists roles only in Barcelona.
Palpable Score
65.3
/ 100
Murphy AI is unusually clear on hiring steps and publishes salary bands plus equity across roles, which reduces guesswork for early-career candidates. The score is held back by a small set of junior-accessible openings and limited public evidence on early-career progression or retention outcomes.
Pillar 1: Early-career access

Score

10.0
/ 20
  • The company has a genuine early-career route with “Associate DevOps Engineer” set at 1+ year of experience and positioned around operational support, runbooks, and incident response rather than senior architecture ownership.
  • Murphy AI’s other technical openings on the main careers feed ask for 3+ years or senior level (for example Implementation Engineer and senior engineering roles), which narrows access for 0–1 year candidates.
  • The company keeps early-career access “sometimes available” rather than recurring, because the live role mix is mostly senior plus one associate-level role.
Pillar 2: Hiring fairness and transparency

Score

16.5
/ 20
  • The company spells out a 4-stage process in job descriptions: first interview, hiring-manager deep dive, a practical assessment or business case, then an in-office coffee with founders.
  • Murphy AI publishes salary ranges inside job posts (for example €30k–€40k for Associate DevOps Engineer and €40k–€70k for Implementation Engineer), which is a strong transparency signal.
  • The company frames the assessment as role-relevant and sets expectations about a “no trick questions” interview style, but the exact time burden for the assessment is not consistently defined.
Pillar 3: Learning and support

Score

13.5
/ 20
  • The company explicitly sells roles with “mentorship” and “continuous development through trainings”, which gives early-career hires a clearer support signal than pure “figure it out” startup language.
  • Murphy AI links learning to real day-to-day behaviours in the Associate DevOps Engineer role, including writing runbooks, knowledge-sharing, and working with defined procedures for incidents and ticketing.
  • The company does not publish concrete onboarding ramps (30/60/90), mentoring time commitments, or progression levels in a way a new grad could verify before joining.
Pillar 4: Pay fairness and stability

Score

16.5
/ 20
  • The company publishes pay bands for multiple roles and pairs this with a stock options statement, including entry-leaning compensation bands on the Associate DevOps Engineer posting.
  • Murphy AI lists practical benefits that support stability for junior staff, including a defined hybrid pattern (3 days office, 2 days remote) and benefits tied to meals and commuting.
  • The company’s benefits are described per role rather than as a single standardised package, which makes it harder to compare offers across teams.
Pillar 5: Early-career outcomes

Score

8.8
/ 20
  • The company has limited independent employee outcome data in public review platforms under the Murphy AI name, so promotion rates, manager quality, and early-career retention are hard to verify externally.
  • Murphy AI’s own careers pages provide a headcount signal (“Coworkers +20”) and at least one third-party company profile lists a team size around the low 20s, but neither shows junior-to-mid progression patterns.
  • The company’s LinkedIn presence supports the view of a small but growing team (size band shown publicly), yet public sources still do not show repeat early-career cohorts or clear early-career promotion stories.
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