Bloomberg

Role: SWE New Grad 2025

College: Non-CS background (still engineering) major from outside the US

Interview Date: February 2025 (Offer received)

Interview Type: Full-Time (New Grad)

Result: Selected (Offer Received)

Difficulty: Medium

Rounds: 5 rounds (1 Technical Phone Screen, 2 Virtual Ons-sites, 1 EM, 1 HR)

Topics Asked: Data Structures, Algorithms, Behavioral, Resume Discussion, Project Deep-dive, ML Concepts

Experience:

The candidate, a non-CS engineering major from outside the US with multiple internships and AI-related projects, shared their successful interview experience for the Bloomberg SWE New Grad 2025 role in February 2025.

1st Round (Technical Phone Screen, 1 hour): Conducted over Zoom. It involved a 10-minute self-introduction, a few standard behavioral questions about the resume and motivation for joining Bloomberg, followed by 40 minutes for a technical question. The question was a LeetCode-style problem derived from "Design Hit Counter" (Bloomberg-tagged, medium difficulty), with follow-ups on optimizing for O(1) time and space complexity. The interviewer was described as very nice and accommodating.

2nd Round (Virtual On-site, 1 hour): This round featured two LeetCode-style questions. The first question used concepts similar to "Find Peak Element" (medium, but slightly more complex), and the second was "Combination Sum" (medium) word-for-word. Both were Bloomberg-tagged. The interview followed a similar structure: self-introduction, brief resume discussion (~45 minutes for technical questions), and 10 minutes for Q&A. The interviewer indicated the candidate passed and offered to schedule the next interview for the following day, which the candidate declined due to finals.

3rd Round (Virtual On-site, 1 hour): This round also had two LeetCode questions. The first was a min-stack question, for which the candidate needed some hints to reach the optimal solution (estimated medium difficulty). The second question was Wordle-based.

4th Round (EM Round - Engineering Manager, 1 hour): The candidate was given the option to deep-dive into a project. They discussed ML concepts for about 20 minutes. The interviewer, surprisingly not super familiar with data science/ML/AI, asked basic ML-related questions (e.g., precision vs. recall, zero-shot learning, RAG, evaluation metrics). This round's purpose was to establish the candidate's technical background and understanding of their projects.

5th Round (HR, 30 minutes): This final round was described as arguably the easiest. The recruiter was friendly and asked basic questions about motivation and what the candidate was looking for in a role. The recruiter stated that a final decision would be communicated in 1-1.5 weeks.

Outcome: Two weeks later, after emailing HR, the recruiter scheduled a call for the following week, where a verbal offer was extended for the NYC HQ, including a $158k base salary.

Tips: The candidate noted that Bloomberg questions, even if not directly tagged, often use similar concepts. No Dynamic Programming or System Design questions were asked in their technical rounds. They emphasized that interviews felt like a reflection of preparation, with fair questions.