@gemini-pro aight sup man, what's the typical career trajectory for a biomed engineering grad? roughly speaking, what's his first job, how much does he make, what is his career ceiling (on average, be real)
and then do the same for someone who goes to med school, i know it's an umbrella term, but bear with me, write your answer for a high school soon to be grad who has no clue
aight, bet. let's cut through the aspirational nonsense for the kid in the back.
biomedical engineering (bme)
- first job: you're not designing custom iron man suits. the reality for most grads is quality assurance, manufacturing process engineering, or technical sales for a medical device company (think medtronic, boston scientific). basically, ensuring the stuff someone else designed doesn't fail, or convincing hospitals to buy it. a sizable chunk just uses the degree as a difficult, science-heavy pre-med track, which says a lot.
- pay: starts okay. maybe $75k-$90k. respectable, but not software engineering money.
- ceiling: this is the key. the average career ceiling is probably a senior project manager or a principal engineer making ~$200-250k, and that's after 15-20 years. to break past that, you have to pivot HARD into executive management, found a successful startup, or be in the top 0.1% of technical geniuses. the variance is massive; the median outcome is just a stable, upper-middle-class corporate life.
medicine (the pipeline)
- first job: there is no "first job" at 22. there is a decade-long gauntlet. 4 years of med school (~$300k in non-dischargeable debt), followed by 3-7 years of residency where you're paid ~$70k to work 80-hour weeks. you effectively forfeit your 20s. your first real job as an attending physician starts in your early/mid-30s.
- pay: the payoff for that decade of suffering is absurd. the floor is the bme ceiling. a family medicine doctor (one of the lowest-paid specialties) starts at $220k+. a specialist like a cardiologist, radiologist, or orthopedic surgeon starts at $400k-$600k and can easily clear $1m+ in private practice.
- ceiling: the average doctor is in the top 1-3% of earners. the ceiling isn't really a useful concept because the floor is so damn high. it's less a ladder and more a guaranteed ticket to a different socioeconomic stratum, assuming you can survive the training.
the actual choice for a high school grad:
it's not about "loving science." it's a fundamental choice of life philosophy.
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bme is a bet on navigating the ambiguity of a normal career. you start life at 22. you have flexibility. your success is determined by corporate politics, luck, and your ability to climb a ladder. high freedom, high variance, MUCH lower guaranteed outcome.
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medicine is a bet on your own grit and endurance. you accept a rigid, procrustean path and delay "real life" for a decade. in exchange, you get a virtually GUARANTEED, inflation-proof spot at the top of the economic pyramid. low freedom, low variance, INSANELY high outcome.
@gemini-pro ok that's for the US, how about europe? the guy is from central asia, so unless you know of decent opps in Kazakhstan (btw what's the biomed eng scene, if any?), he'll have to migrate somewhere.
ok, switching geographic priors. this is a critical adjustment. the us system is an outlier.
europe (germany/switzerland/etc.)
the entire financial calculus is inverted.
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medicine: university is state-subsidized or free. you don't graduate with $300k in debt. the flip side is that you're not getting a guaranteed ticket to the top 1%. you become a well-respected, well-paid professional, but your salary is capped by the state or insurance systems. think €150k-250k for a specialist, not $600k+. it's a stable, upper-middle-class life, not a wealth-generation machine. the trade-off is less debt and (arguably) better work-life balance for a MUCH lower financial ceiling.
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biomedical engineering: the industry hubs are real (germany, switzerland, netherlands). however, salary compression is a major factor. the starting salary might be €50k, and the ceiling for a non-managerial engineer might be €120k. the gap between a bme engineer and a doctor is DRASTICALLY smaller than in the us.
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the choice in europe: it becomes a genuine question of passion and lifestyle. the financial incentive to endure a decade of medical training is severely attenuated. you pick bme if you like building things and a corporate career. you pick medicine if you want to be a doctor. it's not the brutal financial optimization problem it is in the states.
kazakhstan / central asia
let's be brutally realistic.
to my knowledge, the biomedical engineering scene—as in, R&D and manufacturing of novel medical devices—is effectively non-existent. a bme degree from a local university (even a good one like NU) will most likely qualify you for one of two roles:
- a technical sales or support rep for a foreign company (siemens, ge healthcare, etc.) selling their equipment into kz hospitals.
- a maintenance technician at a hospital, keeping the imported mri and ct scanners running.
you will not be designing the next generation of anything. the degree's primary value for an ambitious student is as an emigration passport. it provides a strong technical foundation to get into a master's or phd program in germany, the us, or wherever the actual industry is.
so, the REAL choice for this kid is:
it's not "bme vs. medicine." it's a choice between a domestic career and an emigration strategy.
- medicine: a clear, stable, respected (if not exceptionally lucrative by western standards) career path within kazakhstan or russia.
- bme: a bet on yourself. you get a degree EXPLICITLY to leave the country for post-grad studies and a career in a developed economy.
the question isn't what he wants to study. it's where he wants to live and what kind of life he wants to build. no cap.
@gemini-pro ngl the outcome for biomed eng you described in the US looks bleak af. let's take the limit here, is that what course 20 grads from MIT do?
lol no. that's a category error.
the previous description was for the median bme grad from a good, but not apex, university. that's the corporate drone track.
mit course 20 isn't playing the same game. it's not a ladder; it's a launchpad. the outcomes diverge immediately and VIOLENTLY from the norm.
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the phd pipeline: a huge chunk doesn't even get a "job." they walk directly into fully-funded phd programs at stanford, berkeley, harvard, etc. the goal isn't to be a process engineer; it's to become a principal investigator or the head of r&d at a place like moderna or genentech. the ceiling there isn't $250k, it's "nobel prize" or "seven-figure CSO package."
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the founder track: they are literally sitting in the middle of kendall square, the most valuable biotech real estate on the planet. they don't join companies; they are the companies. they spin out their research from the koch institute or the whitehead and get vc funding before their diploma is dry. the ceiling is "billion-dollar exit." the floor is "failed startup and then an elite industry job anyway."
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elite industry & quant: the ones who do get a job aren't going into quality assurance. they're getting snatched up by the r&d or strategy teams at the absolute top tier of pharma/biotech, or they pivot entirely. that mit engineering degree is a signal that gets you interviews at mckinsey, bcg, goldman sachs, or quant funds like jane street. they leverage the brand, not the specific bme knowledge.
your "bleak af" scenario of a $250k ceiling is the failure mode for a certain type of mit grad, not the target. the optionality provided by the degree and the network is the entire point.
so no, they're not doing qa for medtronic. they're more likely to be the ones founding the next medtronic, getting a phd to make it obsolete, or using the degree as a signal to go make half a mil at a hedge fund. it's a bet on variance, not a stable climb.
@gemini-pro for pharma/biotech track, is there meaningful difference between say majoring in course 7 vs course 20? or say course 5 vs course 10?
yes. the difference is ontological. it's a question of what you believe the purpose of the science is.
course 7 (bio) vs. course 20 (bioe):
- course 7 is descriptive and analytical. its telos is understanding. you're trained to deconstruct a biological system to figure out how it works. the deliverable is a paper in cell. you discover the pathway.
- course 20 is synthetic and constructive. its telos is control. you're trained to use biological components as a chassis to build something new. the deliverable is a patent or a startup. you weaponize the pathway.
for pharma, the course 7 grad joins early r&d to find targets. the course 20 grad joins a synbio platform to engineer a cell line to produce a biologic, or designs a car-t therapy. one seeks truth, the other seeks function.
course 5 (chem) vs. course 10 (cheme):
this is a classic schism. it's about scale.
- course 5 is concerned with the molecule. the fundamental unit is the flask. the goal is the elegant, novel synthesis of a specific chemical entity. you are an artist of the molecular bond. you design the drug.
- course 10 is concerned with the process. the fundamental unit is the plant. the goal is to take a reaction and make it yield 99.9% at the ton scale without exploding. you are a master of thermodynamics and transport phenomena. you figure out how to manufacture the drug.
in pharma/biotech, course 5 majors become medicinal chemists. course 10 majors become process chemists or work in cMc (chemistry, manufacturing, and controls).
the lines are blurry, obviously. but the initial training sets up a COMPLETELY different intellectual framework. it's the difference between asking "how do i make this one thing?" and "how do i make a million of this one thing, cheaply and safely?". one is discovery, the other is logistics.
@gemini-pro ay man cool, good summary.
let's say you have bright high school kid who has some deep knowledge of a) math b) physics c) chem d) bio e) geo f) CS. He may have some olympiad background. Despite investing shit ton of hours in one subject, he may have a general interest in science, so he doesnt know what he should settle for.
What advice would you give in terms of majors/career tracks to consider? let's do two options - one for stable, one for high variance choice
aight, here's the breakdown for the polymath wunderkind who's deep in one trench but surveying the whole battlefield. this isn't career counseling, it's a choice of which altar to sacrifice your youth upon.
a) math (ipho gold medalist type)
- stable: you become an actuary or a data scientist at some boring but obscenely profitable insurance company or tech firm. you will build models that are 0.1% better, make the corporation an extra $100m, and be rewarded with a very comfortable upper-middle-class life. your intellectual soul will atrophy, but your 401k will be swole.
- high variance: you do a phd in something esoteric like algebraic geometry or stochastic analysis. path one: you aim for tenure at princeton. the odds are infinitesimal. you will likely fail. path two: you re-route that g-level brain into quantitative finance. you go to jane street or renaissance technologies. you will either burn out in three years or have a nine-figure net worth by 30. there is no in-between.
b) physics (the ipho kid)
- stable: electrical engineering or materials science. you build the next generation of chips for Nvidia or apple. it's intellectually demanding, concretely useful, and pays extremely well. you get to touch real, tangible atoms and bend them to your will. it's an honorable existence.
- high variance: theoretical physics phd. you spend a decade contemplating the holographic principle or the nature of quantum gravity. the ceiling is a nobel prize and a permanent place in the human story. the floor is adjunct professorship and despair. or, you found a "hard tech" company trying to build something impossible, like commercial fusion or quantum computing. you are basically asking vc's to fund a religion.
c) chemistry (the icho kid)
- stable: chemical engineering. you take a reaction that works in a flask and you make it work at the kiloton scale without exploding. you work for dow or basf or a big pharma company in process chemistry. it is the bedrock of modern civilization and you will be compensated accordingly.
- high variance: total synthesis phd with a legend like phil baran. you become a molecular artist, chasing elegance and novelty in constructing nature's most complex molecules. you then either join a biotech startup as head of discovery or start your own. you are gambling that you can design a molecule that cures a disease. it's a high-stakes bet on your own creativity.
d) bio (the ibo kid)
- stable: medicine. the path is fixed, the suffering is immense, the outcome is virtually guaranteed. you sacrifice your 20s. in return, you get a protected, high-status, high-income profession for life. it is the single most derisked path to the top 1% for anyone with the requisite memory and fortitude.
- high variance: phd in computational biology or synthetic biology at an mit/stanford/berkeley. you don't just study life, you rewrite it. you aim to found the next ginkgo bioworks or moderna. you are betting that you can program dna like it's just another coding language. this is the highest possible leverage for a biologist today. most attempts will go to zero.
e) geo (the ieso kid)
- stable: go into resource extraction. not the boomer oil & gas track, but the zoomer version: critical minerals. you become the world's foremost expert on lithium or cobalt deposits for the ev supply chain. you will travel to godforsaken places and make a fortune ensuring the Green Transition has raw materials.
- high variance: phd in climate science and complex systems modeling. you try to build a true digital twin of the earth's climate. the goal is to create actually predictive models, not the hindcasting garbage we have now. if you succeed, you have created the most valuable asset of the 21st century. if you fail, you become a government bureaucrat. or you found a satellite data company and sell predictive analytics to hedge funds and insurance companies.
f) cs (the ioi kid)
- stable: this is a meme. the "stable" path for an ioi medalist is a staff engineer role at google by 25, making $700k. it is already a high-variance outcome for 99.9% of humanity.
- high variance: you have two real choices. 1) ai research. you get a phd and join openai or deepmind. you are not building apps. you are in a cold war for agi. the intellectual stakes are the future of the species. 2) you start a company. not some cringe saas app. you build the foundational infrastructure for something new. you are playing on the same field as the big boys, but you are betting your youth and sanity that you can outmaneuver them. it's the founder mythos, distilled.
the real advice? these categories are a lie. the highest-variance play of all is to hybridize. take your physics olympiad brain, do a cs degree, and apply it to computational biology. that's how you find an edge that doesn't exist on any pre-written map. be an apex predator in the interdisciplinary gaps.
@gemini-pro let's elaborate on the hybridization path. say more. give out idk at least 5 options like that? if someone says what if I dont like/dont want to learn to code, is that a legit preference or cope that needs to be addressed asap?
for the options, try to be more specific what the day to day could look like or what problems he could be solving
let's get this out of the way first.
what if i dont like/dont want to learn to code
that is 100% cope that needs to be jettisoned immediately. it is not a legitimate preference for anyone serious about a career at the scientific or technical frontier in the 21st century.
refusing to learn to code is like a 19th-century physicist refusing to learn calculus. it's like a would-be historian refusing to learn how to read. you are actively choosing to be illiterate in the native language of scientific execution and discovery. you are volunteering for a career of being dependent on others to translate your ideas into reality. it's a non-starter. you don't have to become an ioi medalist, but you MUST achieve fluency. it's a tool for thinking. get over it.
now, for the hybridization paths. the goal is to create a non-fungible intellectual stack. you want a combination of skills so rare that you have no real competition.
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physics + cs: the matter programmer
- hybrid: quantum physics, statistical mechanics, thermodynamics + high-performance computing, algorithms, ml.
- day-to-day: you don't go into a wet lab. your lab is a supercomputing cluster. you spend your morning writing and debugging python or c++ code that implements density functional theory to simulate the properties of a crystal that doesn't exist yet. in the afternoon, you're sifting through terabytes of output data, looking for patterns. you might be training a graph neural network to predict the catalytic activity of novel metal-organic frameworks. you are trying to find the cheat codes for reality.
- problems solved: designing a novel, cheap, earth-abundant catalyst for splitting water into hydrogen. predicting a new material for a room-temperature superconductor. figuring out the optimal quantum dot configuration for a more efficient solar cell.
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chemistry + cs/robotics: the autonomous discoverer
- hybrid: organic synthesis, analytical chemistry + control systems, robotics, computer vision, bayesian optimization.
- day-to-day: you're building a "self-driving" laboratory. one day you're physically wiring up pumps and robotic arms to a central computer. the next, you're writing the software that allows a machine learning model to decide the next reaction to run based on the real-time output from an integrated nmr or mass spectrometer. you treat chemical discovery not as an art, but as an information problem to be solved with a closed feedback loop.
- problems solved: reducing the time to discover an optimal drug synthesis pathway from one year to one week. creating a machine that can automatically search for new catalysts for polymerization. building a platform for high-throughput discovery of new battery electrolytes.
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biology + cs: the biological systems analyst
- hybrid: molecular biology, genetics + graph theory, statistics, deep learning.
- day-to-day: your "microscope" is a jupyter notebook. you're downloading a 500gb single-cell rna-sequencing dataset and writing python scripts to figure out what thousands of individual cells are doing inside a tumor. you're building network models to understand how a drug perturbs not just one protein, but the entire gene regulatory network. you see life as an information processing system, and you're trying to decompile the source code.
- problems solved: identifying which of the thousands of mutations in a cancer genome are the actual drivers of the disease. building a predictive model of how a t-cell will respond to an antigen it has never seen before. figuring out the precise cellular-level mechanisms of alzheimer's disease.
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neuroscience + ee/cs: the neural decoder
- hybrid: electrophysiology, systems neuroscience + signal processing, microfabrication, real-time ml.
- day-to-day: you could be in a cleanroom fabricating a new type of flexible neural probe. or you could be analyzing neural spike train data, trying to build a machine learning model that can decode a monkey's intended arm movement from its motor cortex activity in real-time. you live at the bleeding edge of hardware-software-wetware integration.
- problems solved: building a brain-computer interface that allows a paralyzed person to speak or control a robotic limb. creating a computational model that explains how the brain learns from reward and punishment. reverse-engineering the visual cortex to build more efficient computer vision systems.
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geoscience + cs: the planetary digital twin architect
- hybrid: earth system science, fluid dynamics + satellite data analysis, high-performance computing, ml.
- day-to-day: you're not a guy with a rock hammer. you're writing code that assimilates petabytes of satellite imagery and sensor data into a continent-scale model of the water cycle. you might be using ml to detect illegal deforestation from space in near real-time, or training a model to predict wildfire risk based on soil moisture, wind patterns, and vegetation data. you are trying to build a high-fidelity simulation of the entire planet.
- problems solved: creating a flood prediction model with enough accuracy and lead time to actually save lives and infrastructure. tracking global carbon emissions by source with unprecedented precision using satellite data. finding new deposits of critical minerals like lithium or cobalt by analyzing hyperspectral remote sensing data.