Key Takeaways
- Schrödinger is a NYC-headquartered computational drug discovery and materials science company (NASDAQ: SDGR) with a hybrid software-plus-biotech model.
- The scientific moat is physics-based simulation — Maestro, FEP+, Glide, Desmond — not generative AI, though ML is integrated thoughtfully.
- Founded 1990 by Richard Friesner and William Goddard; named after Erwin Schrödinger; Ramy Farid CEO since 2017.
- Apply via schrodinger.com/careers; some roles appear on Greenhouse; resumes should be text-based and domain-specific.
- Expect an academic-style loop: job talks for scientists, rigorous technical rounds for engineers, behavioral screens throughout.
- Many science roles expect a PhD; software roles value scientific-computing correctness over web-stack fluency.
- Biotech market conditions since 2022 have pressured SDGR stock and collaboration revenue; software line is steadier — understand the business mix before joining.
- The culture is direct, PhD-dense, and meritocratic; if you miss the intellectual rigor of academia, you will likely feel at home.
About Schrödinger
Schrödinger, Inc. (NASDAQ: SDGR) is a computational drug discovery and materials science company headquartered in New York City. Founded in 1990 by Richard Friesner (a Columbia University theoretical chemist) and William Goddard, the company is named after Erwin Schrödinger, the Austrian physicist whose wave equation underpins modern quantum chemistry. That naming is not a marketing flourish — Schrödinger's technical moat is physics-based molecular simulation, and the science lineage runs through Columbia, Caltech, and the broader academic computational chemistry community. Ramy Farid, PhD has served as CEO since 2017, succeeding the research-led leadership that built the early platform.
The business runs two linked engines. The first is a software platform licensed to pharmaceutical and materials companies — the flagship is Maestro (the molecular modeling GUI), wrapping tools like Glide (molecular docking), FEP+ (free energy perturbation, widely considered the gold standard for binding affinity prediction), LiveDesign (the collaborative team platform), Phase (virtual screening), Desmond (molecular dynamics), Jaguar (DFT/quantum mechanics), and the Materials Science Suite (batteries, catalysts, OLEDs). The second engine is internal and partnered drug discovery: Schrödinger runs its own pipeline (SGR-1505 targeting MALT1, SGR-2921 targeting CDC7 in AML, SGR-3515 targeting Wee1/Myt1 in solid tumors) and signs large collaboration deals — Bristol Myers Squibb (~$2.7B, 2020), Otsuka, Eli Lilly (2023), Sanofi (2023), Zai Lab, Takeda, and others.
Headcount is roughly 800+ globally with offices in New York, Cambridge MA, Portland OR, San Diego, Mountain View, New Delhi, Hyderabad, Tokyo, London, and Mannheim Germany. Schrödinger went public in February 2020 at $17/share, raising approximately $232M. Bill Gates's Cascade Investment holds a major (12%+ pre-IPO) position, and David E. Shaw of D.E. Shaw Research (a related but legally separate entity) has been historically connected with Schrödinger's founding science. Be honest with yourself about what you are joining: a hybrid software-plus-biotech company whose revenue mix is lumpy (software is recurring and growing; collaboration revenue and royalties are milestone-driven and volatile), operating in a biotech capital market that has been under pressure since 2022. SDGR's stock volatility reflects that, not the underlying science quality.
Application Process
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1
Apply through schrodinger
Apply through schrodinger.com/careers — the in-house portal is the canonical source; some roles (especially engineering) occasionally surface through Greenhouse.
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2
Pick the right ladder: Scientist (PhD expected for most roles), Software Enginee
Pick the right ladder: Scientist (PhD expected for most roles), Software Engineer (MS/BS acceptable, strong CS fundamentals required), Applications Scientist (PhD plus customer-facing skill), Business/GTM (domain experience in life sciences matters).
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3
Tailor your resume to the specific posting
Tailor your resume to the specific posting — a FEP+ methods role and a platform infrastructure role look for different signals; do not send a generic CV.
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4
Expect a recruiter phone screen first (30 minutes), focused on motivation, backg
Expect a recruiter phone screen first (30 minutes), focused on motivation, background fit, and logistics — visa status, location, comp expectations.
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5
Technical screen follows: coding interview for SWE, methods/chemistry discussion
Technical screen follows: coding interview for SWE, methods/chemistry discussion for scientists, portfolio walkthrough for product and design.
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Onsite (now often virtual) is typically a half to full day: multiple technical r
Onsite (now often virtual) is typically a half to full day: multiple technical rounds, a presentation (scientists present their PhD or recent work for ~45 minutes), and behavioral conversations.
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For scientist roles, the job talk is decisive
For scientist roles, the job talk is decisive — it is evaluated like a departmental seminar, not a corporate pitch; expect rigorous Q&A from PhDs across disciplines.
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Reference checks are thorough and often include academic advisors for recent-PhD
Reference checks are thorough and often include academic advisors for recent-PhD candidates.
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Offers typically include base salary, annual bonus, and RSUs; negotiate on RSU g
Offers typically include base salary, annual bonus, and RSUs; negotiate on RSU grant size and vesting cliff, not just base.
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Timeline from first contact to offer is commonly 4 to 8 weeks; slower for senior
Timeline from first contact to offer is commonly 4 to 8 weeks; slower for senior scientist roles where committee review is involved.
Resume Tips for Schrödinger
recommended
Lead with a concise summary that names the scientific or engineering domain (e
Lead with a concise summary that names the scientific or engineering domain (e.g., 'Free energy methods development' or 'Distributed systems for scientific compute') — generic summaries get filtered.
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For scientist roles, list publications with first-author and corresponding-autho
For scientist roles, list publications with first-author and corresponding-author status clearly marked; include citation counts only if meaningful (>20) and recent.
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Name specific tools and methods you have used: Schrödinger's own suite (Maestro,
Name specific tools and methods you have used: Schrödinger's own suite (Maestro, Glide, FEP+, Desmond), plus adjacent stacks (OpenMM, GROMACS, RDKit, AMBER, Gaussian, ORCA, PyTorch for ML roles).
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Quantify outcomes: 'Reduced FEP calculation error from 1
Quantify outcomes: 'Reduced FEP calculation error from 1.2 to 0.8 kcal/mol across 40-compound benchmark' beats 'improved binding prediction accuracy.'
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For SWE roles, emphasize scale and correctness: HPC scheduling, GPU orchestratio
For SWE roles, emphasize scale and correctness: HPC scheduling, GPU orchestration, scientific data pipelines, numerical stability — not generic CRUD experience.
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Link your GitHub, Google Scholar, and ORCID where relevant — this is an academic
Link your GitHub, Google Scholar, and ORCID where relevant — this is an academic-adjacent environment and the team will look.
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Keep it to one page for junior candidates, two for senior PhDs with publication
Keep it to one page for junior candidates, two for senior PhDs with publication lists; a long CV format is acceptable for senior scientists but not required.
recommended
Do not oversell ML/AI experience unless you have it — Schrödinger is physics-bas
Do not oversell ML/AI experience unless you have it — Schrödinger is physics-based and respects honest scope; claiming 'AI-driven drug discovery' when you ran one Jupyter notebook is a tell.
recommended
Call out cross-functional collaboration: the platform ships to medicinal chemist
Call out cross-functional collaboration: the platform ships to medicinal chemists, so examples of working with wet-lab or translational teams carry weight.
recommended
Use plain, ATS-parseable formatting; the company uses a custom careers portal, a
Use plain, ATS-parseable formatting; the company uses a custom careers portal, and overly designed PDFs can still lose information.
ATS System: Schrödinger Custom Careers Portal (schrodinger.com/careers) with occasional Greenhouse usage
Schrödinger runs an in-house careers portal at schrodinger.com/careers that handles most requisitions directly. A subset of roles — typically engineering or corporate — appears on Greenhouse under the schrodinger board. Expect a standard application form with resume upload, cover letter field, and domain-specific screening questions (legal authorization, location, seniority, relocation).
- Upload a plain PDF or DOCX with machine-readable text — not a scanned or heavily-designed layout.
- Fill in every optional field, including LinkedIn, GitHub, and Google Scholar, where applicable.
- Answer screening questions precisely — 'authorized to work in the US without sponsorship' is a literal filter.
- If the role appears on Greenhouse, the same guidance applies; both systems parse text-based content best.
- Use exact keyword matches from the job description for your top skills, but do not keyword-stuff — recruiters read every resume at this tier.
- Recheck the careers site weekly; scientist roles post on rolling cycles, and some senior openings are filled before wider broadcast.
Interview Culture
Schrödinger's interview loops feel closer to an academic department than a typical tech company.
For scientist roles, the centerpiece is a job talk — you present recent research (usually your PhD or postdoc work) for 45 minutes to a mixed audience of computational chemists, methods developers, and applications scientists. Expect sharp, informed questions about methodology, statistical handling, physical interpretation, and limitations. The panel is looking for rigor and scientific taste, not polish.
For software engineers, expect strong CS fundamentals — data structures, algorithms, systems design — with a bias toward numerical correctness, performance, and scientific-computing concerns (GPU kernels, HPC scheduling, reproducibility, floating-point behavior). A common failure mode is candidates who are strong web developers but have never thought about why a float sum is non-associative. Applications scientist and scientific services roles blend technical depth with customer-facing ability; you will present and also be evaluated on how you explain hard ideas to a medicinal chemist who does not care about your Hamiltonian.
Behaviorally, the culture is direct, PhD-heavy, and meritocratic in the academic sense — seniority is earned through demonstrated ability, and pedigree from Columbia, Stanford, MIT, Caltech, Berkeley, and similar programs is common but not mandatory. The tone is more professorial than corporate; decisions are argued from evidence, and hand-waving is punished politely but firmly. If you dislike having your claims pushed on, this is not the right environment. If you have missed academia's intellectual atmosphere since leaving, you will likely feel at home.
What Schrödinger Looks For
- Scientific rigor — the ability to defend a methodological choice with physical reasoning and data, not vibes.
- Deep expertise in a core area: molecular dynamics, free energy methods, cheminformatics, quantum chemistry, ML for chemistry, HPC, or distributed systems.
- Intellectual humility paired with conviction — willing to be wrong in a meeting, unwilling to ship something you cannot defend.
- For scientists: a strong publication record and credible PhD advisor, though outlier industrial backgrounds are considered.
- For engineers: production experience with systems that matter for correctness (scientific compute, data platforms, numerical libraries) over pure web stack fluency.
- Ability to communicate across the software-science boundary — chemists and engineers have to co-design here.
- Comfort with a hybrid business model where some of the revenue is recurring software and some is milestone-driven biotech.
- A realistic view of AI in drug discovery — Schrödinger's physics-based approach respects ML but does not treat it as a magic wand; generative-AI maximalism without physical grounding is a negative signal.
- Long-term orientation — drug programs run for years and the company takes a generational view of its platform, so short-termers struggle.
- Ownership mindset: individual contributors are expected to drive their scope, not wait to be told.
Frequently Asked Questions
Do I need a PhD to work at Schrödinger?
For most scientist roles, yes — computational chemist, methods developer, applications scientist, and similar titles typically require a PhD in computational chemistry, biophysics, or a closely related field. Software engineering, platform, infrastructure, product, design, and business roles do not require a PhD; strong MS and BS candidates are common on those ladders. Check the specific posting for the exact requirement.
What is Schrödinger's relationship with D.E. Shaw Research?
They are related but legally separate entities. David E. Shaw has been historically connected with Schrödinger's founding science, but D.E. Shaw Research (which builds the Anton molecular dynamics supercomputers) is independent. Do not conflate them in an interview; it signals that you have not done your homework.
Is Schrödinger an AI company or a physics company?
Physics-first, with ML integrated where it improves results. Schrödinger's core methods (FEP+, Glide, Desmond, Jaguar) are rooted in physical simulation. The company uses machine learning to accelerate and augment those methods rather than replace them. Candidates who pitch themselves as 'AI drug discovery' without physical grounding tend to underperform in interviews.
How does the business model actually work?
Two lines. (1) Software: pharma and materials companies license the Maestro-based platform for in-house use — this is the recurring revenue engine and has grown steadily. (2) Drug discovery: Schrödinger runs its own internal pipeline (SGR-1505, SGR-2921, SGR-3515) and runs collaborations with partners like BMS, Lilly, Sanofi, Otsuka, Zai Lab, and Takeda. Collaboration revenue is milestone-driven and lumpy. Understand both lines before an interview; they shape the company's strategy and risk profile.
What is SDGR's stock doing and should I care?
SDGR has been volatile since the 2022 biotech pullback, as has almost every platform-biotech name. The software line has been resilient; the collaboration and royalty line is sensitive to pipeline timing and broader biotech sentiment. If you are joining for equity, size the RSU component accordingly and do not assume any particular trajectory. The science quality and long-term platform value are not the same as the stock quote.
Where is Schrödinger based and can I work remotely?
Headquarters is New York City, with major offices in Cambridge MA, Portland OR, San Diego, Mountain View, New Delhi, Hyderabad, Tokyo, London, and Mannheim Germany. Some roles are remote or hybrid; many are tied to a specific office, especially for scientists who collaborate closely with specific teams. Check the posting and ask the recruiter directly — do not assume.
Who are Schrödinger's main competitors?
In software: OpenEye Scientific (acquired by Cadence in 2023), Chemaxon, Cresset, CCG/MOE, and increasingly AWS and cloud HPC providers that offer commodity compute. In AI-first drug discovery: Isomorphic Labs (Alphabet), Insitro, Recursion (which merged with Exscientia in 2024), Genesis Therapeutics, Iambic Therapeutics, Atomwise, Relay Therapeutics. The physics-vs-AI debate is converging; Schrödinger sits on the physics-first side and integrates ML thoughtfully.
What is the interview process like for scientists?
Recruiter call, technical phone screen, then an onsite (often virtual) that includes a 45-minute job talk on your recent research, multiple technical rounds with computational chemists and methods developers, and behavioral conversations. The job talk is decisive — it is evaluated like an academic seminar with rigorous Q&A. Practice it. Time it. Handle the tough questions gracefully; defensiveness reads worse than 'I do not know yet.'
What is the interview process like for software engineers?
Recruiter call, technical phone screen with coding, then an onsite that includes multiple coding rounds, systems design (often scientific-computing flavored — think GPU orchestration, HPC scheduling, numerical correctness), and behavioral. Strong CS fundamentals are table stakes; familiarity with scientific computing, numerical stability, or biotech domain is a significant differentiator.
Does Schrödinger sponsor visas?
Historically yes for many roles, especially senior scientists and engineers. Policies can change; confirm with the recruiter during the initial screen. Do not assume sponsorship on a given posting — the application form usually asks directly, and you should answer honestly.
What tools should I be familiar with before interviewing?
Schrödinger's own suite where applicable: Maestro, Glide, FEP+, Desmond, Jaguar, LiveDesign, Phase. Adjacent open-source stacks demonstrate range: OpenMM, GROMACS, AMBER, RDKit, ORCA, Gaussian, PyTorch or JAX for ML work. For SWE roles: Python, C++, CUDA or GPU programming, container orchestration, and distributed compute. You do not need to be expert in everything; you do need to show depth in the area you are being hired for.
Is the Bill Gates / Cascade Investment connection real?
Yes. Cascade Investment, Bill Gates's investment vehicle, held a major (12%+) position pre-IPO and has remained a significant shareholder. It is not a governance or day-to-day operational involvement — it is an investment — but it shaped the company's long-term capital posture through IPO.
Open Positions
Schrödinger currently has 2 open positions.
Related Resources
Sources
- Schrödinger, Inc. — Official Careers Site —
- Schrödinger, Inc. — Investor Relations —
- Schrödinger Platform Overview —
- Schrödinger S-1 IPO Filing (February 2020) —
- Schrödinger — Bristol Myers Squibb Collaboration Announcement (2020) —
- Schrödinger Pipeline Overview (SGR-1505, SGR-2921, SGR-3515) —
- Schrödinger Leadership — Ramy Farid, PhD —
- FEP+ Free Energy Perturbation Methodology —
- Schrödinger LinkedIn Company Profile —
- Cascade Investment (Bill Gates) Schrödinger Holdings Disclosure —
- Schrödinger — Eli Lilly Collaboration Announcement (2023) —
- Schrödinger Materials Science Suite —
