Interested in this Research Scientist role at Onepot?
Apply Now →About This Role
onepot is automating chemistry. Our goal is to enable a self\-improvement loop for chemistry by combining AI and advanced robotics. This goal can only be achieved by bringing people of various backgrounds together — ML engineers, chemists, computer scientists, and hardware engineers. Our current team comprises of the best people in their respective fields, and we are looking for a new team member.
The focus of the job is to bring new reactions, transformations, and capabilities to the platform. This can be done by working closely with and improving our AI agent Phil, as well as working on the robotic set up. Additionally, we will ask you to work on custom projects in cases, where automated platform is not good enough yet.
The role
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In this position, you will bring new reactions, transformations, and capabilities onto onepot's automated platform, and make them work reliably at scale.
The work spans bench chemistry and platform development. You will develop and optimize routes at the bench, then translate what you learn into something the platform can execute repeatedly — working closely with our AI agent Phil and with the robotic setup itself. Where the automated platform is not yet good enough, you will run the chemistry manually and say what would have to change for it not to be.
This is a hands\-on role. You will design and run experiments, interpret your own analytical data, scale up routes for customer orders, and publish what is worth publishing. You will own transformations from first literature search through validated, automated execution.
This is not a role where you hand a protocol to someone else and move on. We are looking for a chemist who wants their chemistry running on the platform, and who will keep working on it until it does.
Education
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- PhD in organic chemistry or equivalent experience
- Extensive publication record on frontier organic chemistry work
Skills
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### Organic chemistry knowledge
- Mechanisms and named reactions
- Understanding of key theoretical concepts such as molecular orbital theory, basic physical organic chemistry concepts (e.g., free energy linear relationships, kinetic isotope effects)
- Transition metal catalysis
- Radical transformations
- Ability to interpret analytical data (see below)
### Synthetic chemistry experience
- Basic synthetic organic chemistry workflows
- Schlenk and glovebox experience
- Preferred — experience with photochemical and electrochemical transformations
- Preferred — experience with automation platforms (Opentrons, Hamilton, Tecan, etc.)
- If you want to avoid experimental work in your next career steps this job is not a good fit
### Analytical techniques
- NMR — fluency in analysis of data from a simple proton NMR spectrum to complex 2D NMR experiments. Ability to execute in situ reaction monitoring. Good check: do I know what DOSY is?
- Liquid chromatography — understanding how different mobile and stationary phases influence analytical outcome. How to improve separation? How to make something that does not retain on the column? Ability to do both normal phase and RP separations, experience in both.
- Mass spectrometry — clear understanding of different ionization methods, fragmentation possibilities in those, particularly in soft ionization methods. Some experience with maintaining those systems.
### Soft skills
- Excellent communication, ability to communicate with customers on complex scientific questions
- Ability to convert your understanding of a problem into a narrower set of rules/instructions, that could be picked up by an AI agent. Not “I feel like it”, but “here is why this peak corresponds to this proton”
- Ability to identify issues and work towards solving that. Not “the data did not get synced, can someone fix that”, but work with a company’s AI agent to troubleshoot the issue and only escalate when no other options left.
Who will thrive here
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You may be a strong fit if you:
- Ask how a reaction would run two hundred times unattended while you are still developing it at the bench
- Cannot accept a result you cannot explain — you chase the side product rather than report the yield and move on
- Want your hands in the fume hood and your chemistry on the platform, not one or the other
- Would rather find the next transformation worth chasing than work through a queue someone else wrote
- Treat the robots and our AI agents as your instruments rather than someone else's problem
- Want substantial responsibility early, including over how the work gets structured
- Are willing to work outside a narrow job description to make the overall system succeed
Additional requirements
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- You might be asked to travel across the country for a conference
- You might be asked to stay late if there are urgent customer orders
- You might be asked to stay over weekend if urgent work was not completed over the week
- Previous two aspects are preventable with proper scheduling of work, if that happens, we expect you to identify those issues and correct them down the road
- You are expected to learn new technologies involving internal AI tools and lab’s custom hardware \& automation. Our team is very interdisciplinary and learning from other team member as well as sharing your knowledge with the rest of the team is very important.
- We are not a clock\-in, clock\-out environment. We expect substantial ownership and flexibility: sometimes that means leaving early, and sometimes it means staying late to finish an experiment or meet an important customer commitment. We evaluate people primarily on judgment, output, and whether they step up when the work genuinely requires it—not on performative hours.
Work activity distribution
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- 35% existing customer projects — manual rerun, scale up
- 45% research of new transformations and automation integration
- 10% lab operations support
- 10% preparing scientific publications
Benefits
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- Lunches and dinners (if staying late) in office
- Commute stipend
- Top\-of\-the\-line insurance
- Generous equity grants
onepot is an equal\-opportunity employer.
Compensation Range: $170K \- $220K
Salary Context
This $170K-$220K range is above the median for Research Scientist roles in our dataset (median: $195K across 149 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 4,317 AI roles we're tracking, Research Scientist positions make up 4% of the market. At Onepot, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills in Demand for This Role
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 378 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($195K) sits 12% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Onepot AI Hiring
Onepot has 2 open AI roles right now. They're hiring across Research Scientist. Based in South San Francisco, CA, US. Compensation range: $220K - $250K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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