u/Certain-Reputation-1

I’m trying to compare two potential full-time paths after internships:

SIG — Quantitative Strategy Developer (QSD)
OpenAI — Applied SWE

From what I understand, OpenAI’s entry-level Applied SWE return offer is around $250k TC.

For SIG QSD, I’ve seen people on Reddit claim that first-year new-grad TC can be around $600k, particularly for QSD return offers. That number surprised me because it’s substantially higher than the more general SIG SWE/quant developer compensation numbers online.

Can anyone who is familiar with recent SIG QSD offers confirm the approximate new-grad compensation range?

Specifically:

What is typical first-year QSD TC?

How much is base vs sign-on/guaranteed bonus vs discretionary bonus?

Is ~$600k actually realistic for a new-grad QSD return offer, or is that an outlier/inaccurate?

How does compensation progress after years 1–3?

How does QSD compare with SIG QT/QR compensation?

Career-wise, I’m also trying to decide between QSD and OpenAI Applied SWE. QSD seems interesting because it’s closer to trading strategies/signals and potentially provides a path toward QR/QT, while OpenAI obviously offers exposure to frontier AI and a strong SWE/RE trajectory.

If you had both offers, which would you take and why?
Mainly interested in hearing from people familiar with actual recent QSD offers, rather than general SIG SWE compensation data.

I’m an incoming transfer student at UC Berkeley for Applied Mathematics, and I’m trying to figure out whether it’s realistically possible to finish my degree while working full time as a software engineer in San Francisco.

From what I’ve found, L&S allows a reduced course load for students working 15+ hours/week, so administratively it seems possible. My bigger concern is the actual scheduling.

Most of the required upper-division Applied Math courses (Math 104, 110, 113, 128A, 185 + 3 cluster electives) seem to meet during normal weekday working hours. Since I’d be working full-time in SF, I’d probably need to prioritize 8 AM/late-afternoon sections, online courses when available, and potentially Summer Sessions.

Ideally, I’d like to take around 6–8 units per semester and still finish within ~2 years rather than stretching the degree out for 3–4+ years.

Has anyone here done something similar at Berkeley?

Specifically wondering:

How realistic is 6–8 units of upper div math while working ~40 hours/week?

Are professors/classes generally flexible enough for someone commuting between Berkeley and SF?

How often are Math 104/110/113/128A/185 available early morning, late afternoon, or online?

Is it realistic to use Summer Sessions to speed this up while continuing to work?

Are there any residency, reduced course-load, or graduation rules that could make this plan difficult?

If you’ve worked full-time while attending Berkeley part-time, what did your weekly schedule look like?

I’m less worried about the workload itself than whether the actual class meeting times can be made compatible with a full-time engineering schedule.

Would appreciate hearing from anyone who’s done something similar, especially Applied Math/Math/L&S transfer students.