Company details

HEADCOUNT

25-99

ORG TYPE

Startup

SECTOR

Healthcare & Life Sciences

WEBSITE

https://www.cradle.bio

About the company

Cradle builds an AI-guided protein engineering platform used to design and optimize proteins for applications across areas like therapeutics, diagnostics, chemicals, food, and agriculture. The company talks openly about running a tight “lab-in-the-loop” setup, where in-house wet-lab work helps validate models and shorten iteration cycles. Cradle has scaled beyond a founding team into a multi-discipline org spanning biology, machine learning, engineering, and product, with hiring across Amsterdam, Zurich, and some remote commercial roles.

Locations and presence

Cradle is anchored in Amsterdam and Zurich, with roles posted for both hubs. The company also hires for some remote positions, including US-based commercial and customer-facing roles.

Palpable Score

62.0

/ 100

Cradle offers a real on-ramp via research associate roles that sit in the core product engine, plus job descriptions that spell out responsibilities and expectations with fewer “mystery box” vibes than many startups. Scores are held back mainly by limited public evidence on junior progression, retention, and salary transparency across most roles.

Pillar 1: Early-career access

Score

12.0

/ 20

The company advertises Research Associate roles with requirements like “BSc + 2 yrs” or “MSc,” which is a workable early-career entry point (especially for lab-based grads).
Cradle’s open roles skew senior across science, ML, and engineering, so early-career hiring looks more occasional than repeatable and high-volume.
The company’s early-career access is strongest in bioengineering lab execution roles, with fewer obvious junior tracks in other functions from publicly visible listings.

Pillar 2: Hiring fairness and transparency

Score

14.5

/ 20

The company’s job descriptions tend to be specific about day-to-day responsibilities (equipment, workflows, collaboration, deliverables), which helps candidates self-select.
Cradle includes inclusive hiring language and “missing one or two points… still apply” messaging, which reduces credential gatekeeping in practice.
The company provides anti-scam guidance and clarifies offer norms, but most listings do not publicly share the interview stages or consistent salary ranges, limiting transparency.

Pillar 3: Learning and support

Score

12.5

/ 20

The company explicitly frames learning as part of the work, including cross-functional collaboration between wet lab teams and ML teams in several role write-ups.
Cradle publishes cultural values that include expectations around teaching, blameless learning, and sharing knowledge, which is a helpful support signal for juniors if applied in practice.
The company links candidates to team-written resources (blog posts, webinars) to understand the work, but public materials rarely spell out onboarding plans, mentorship structure, or review cadence for early-career hires.

Pillar 4: Pay fairness and stability

Score

13.5

/ 20

The company hires most roles as full-time and repeatedly references equity plus benefits, which is a stability signal compared with short-contract churn models.
Cradle occasionally shows salary ranges on external postings, but ranges are not consistently published across roles, which caps confidence for early-career pay fairness.
The company positions compensation as “top of market” or “very competitive,” but without role-by-role ranges, candidates have to do more negotiation work than ideal.

Pillar 5: Early-career outcomes

Score

9.5

/ 20

The company publishes employee interviews and team narratives, which gives some visibility into who joins Cradle, but these pieces do not quantify promotions, tenure, or junior progression outcomes.
Cradle appears to be hiring repeatedly across teams and locations, suggesting growth, but public evidence on early-career retention over 12–24 months is thin.
The company’s LinkedIn presence and headcount indicators show scale-up momentum, yet public outcome data like junior promotion examples, leveling, or retention metrics is missing, which limits this score.