Inceptive

At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team.
We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models depend on rich, high-quality experimental data that captures biological function. Progress requires not only building better models, but also designing better experiments, understanding measurement systems, and generating datasets that faithfully represent underlying biology.
You will collaborate closely with biologists and machine learning researchers to design, analyze, and improve the experiments that power our models. You will help determine what data should be generated, how experiments should be structured, how measurement artifacts can be identified, and how biological insights can be translated into scalable data generation strategies.
Your Mission, should you choose to accept it
<ul>
<li>Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise</li>
<li>Develop statistical and computational approaches to characterize assay quality, reproducibility, and sources of experimental variation</li>
<li>Identify and investigate sources of bias and measurement artifacts in biological datasets</li>
<li>Design and analyze large-scale biological experiments that generate training and evaluation data for machine learning models</li>
<li>Partner with experimental scientists to improve assay design, controls, and data collection strategies</li>
<li>Collaborate with machine learning researchers to understand how experimental design decisions impact model training and evaluation</li>
<li>Analyze, visualize, and communicate findings to support decision-making across scientific and engineering teams</li>
</ul>
Qualifications
<ul>
<li>PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics, or a related quantitative discipline, or equivalent practical experience</li>
<li>Demonstrated track record of analyzing complex biological datasets and translating computational insights into experimental validation or new data collection</li>
<li>Strong foundation in experimental design, statistical analysis, and quantitative reasoning</li>
<li>Deep understanding of sources of experimental variability, batch effects, and assay artifacts in biological data</li>
<li>Capable programmer in Python and common scientific computing libraries</li>
<li>Excellent written and verbal communication skills, including the ability to communicate effectively across computational and experimental disciplines</li>
<li>Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET</li>
<li>Readiness to travel several times a year for company retreats and business events</li>
<li>We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations</li>
</ul>
Preferred technical skills
<ul>
<li>3+ years of post-PhD experience in computational biology, biostatistics, or a related field</li>
<li>Experience connecting experimental outcomes to machine learning model development and evaluation</li>
</ul>
Compensation
$135K – $240K + Bonus + Equity
 <div>What we offer
<ul>
<li>A competitive compensation package</li>
<li>30 days paid vacation per year</li>
<li>Comprehensive health insurance for US based Beginners</li>
<li>401K with company match for US based Beginners and Direktversicherung for German Beginners</li>
<li>Quarterly company-wide retreats</li>
<li>Monthly wellness benefit</li>
<li>Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland</li>
<li>Learning &amp; Development budget to attend conferences, take courses, or otherwise invest in your professional growth</li>
<li>A buddy to help you get settled</li>
</ul>
*Varies by country and does not apply to internships
At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.
We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.
It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment.
Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.</div>

Data selection and quality evaluation for biological foundation models

At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team.
We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models learn from diverse biological datasets and are refined through focused experimentation, large-scale training, and feedback from lab measurements. Progress depends not only on building better models, but also on understanding what they learn, where they fail, how data shapes their behavior, and how to evaluate them against biologically meaningful objectives.
You will collaborate closely with AI researchers and biologists to rigorously characterize the behavior, capabilities, and limitations of biological foundation models and their applications. You will identify valuable datasets, develop meaningful evaluations, investigate model behavior, and generate insights that guide model development, data strategy, and experimental priorities across the company.
Your Mission, should you choose to accept it
<ul>
<li>Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise</li>
<li>Investigate how model performance changes with data quantity, data quality, dataset composition, and training methodology</li>
<li>Develop biologically meaningful evaluations and benchmarks that measure progress toward therapeutic design objectives</li>
<li>Design and execute rigorous experiments to understand the behavior, capabilities, and limitations of biological foundation models</li>
<li>Identify sources of potential artifacts, bias, and noise in biological datasets</li>
<li>Identify promising biological datasets for model training and evaluation, and develop computational pipelines for preprocessing, quality control, and exploratory analysis.</li>
<li>Design studies, in silico or in the lab, that reveal what models have learned and which biological signals drive model behavior</li>
<li>Work with biologists to formulate hypotheses and translate biological questions into measurable machine learning experiments</li>
<li>Partner with AI researchers and engineers to prioritize research directions, data collection efforts, and model improvements</li>
<li>Analyze, visualize, and communicate experimental findings to inform decisions across teams</li>
</ul>
Qualifications
<ul>
<li>PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience, with record of publications or open source tooling in these fields</li>
<li>Strong quantitative reasoning and statistical intuition</li>
<li>Demonstrated ability to identify important scientific questions, design rigorous investigations, and draw reliable conclusions from complex biological data.</li>
<li>Experience analyzing high throughput sequencing data (e.g. RNA-seq, functional genomics / transcriptomics, MPRA), with a focus on robust statistical analysis</li>
<li>Experience collaborating closely with AI/machine learning researchers or applying machine learning or generative AI tools to scientific problems</li>
<li>Familiarity with current AI/machine learning methods, including generative foundation models, representation learning, and model evaluation</li>
<li>Familiarity with publicly available biological datasets and data derived from high throughput assays</li>
<li>Capable programmer in Python and common scientific computing libraries</li>
<li>Excellent written and verbal communication skills, including the ability to explain complex findings to audiences with diverse technical backgrounds</li>
<li>Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET</li>
<li>Readiness to travel several times a year for company retreats and business events</li>
<li>We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations</li>
</ul>
Preferred technical skills
<ul>
<li>3+ years of post-PhD experience in computational biology, biostatistics, machine learning research, or a related field</li>
</ul>
Compensation
$135K – $240K + Bonus + Equity
 <div>What we offer
<ul>
<li>A competitive compensation package</li>
<li>30 days paid vacation per year</li>
<li>Comprehensive health insurance for US based Beginners</li>
<li>401K with company match for US based Beginners and Direktversicherung for German Beginners</li>
<li>Quarterly company-wide retreats</li>
<li>Monthly wellness benefit</li>
<li>Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland</li>
<li>Learning &amp; Development budget to attend conferences, take courses, or otherwise invest in your professional growth</li>
<li>A buddy to help you get settled</li>
</ul>
*Varies by country and does not apply to internships
At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.
We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.
It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment.
Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.</div>

Computational design of biological experiments for model development

At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0010006" rel="noopener noreferrer ugc" target="_blank">antedisciplinary</a> team building our biological software.
The design space of biomolecules is unimaginably vast — far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.
We advance the state of the art in molecular design by training large-scale foundation models and developing cutting edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.
You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.
<h3>Your Mission, should you choose to accept it</h3>
<ul>
<li>Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise</li>
<li>Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design</li>
<li>Analyze, visualize, and communicate results to support team efforts in improving models and data</li>
<li>Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production</li>
<li>Work with biologists to collect data for the training and evaluation of generative models of biomolecules</li>
<li>Provide mentorship and technical direction to team members as appropriate</li>
</ul>
<h3>Qualifications</h3>
<ul>
<li>3+ years of hands-on experience developing ML models</li>
<li>Demonstrated track record of implementing, training, improving advanced machine learning models</li>
<li>Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework</li>
<li>Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET</li>
<li>Readiness to travel several times a year for company retreats and business events</li>
<li>We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations</li>
</ul>
<h3>Preferred technical skills</h3>
<ul>
<li>PhD in AI/ML, computer science, computational biology, physics, or a related field, or equivalent practical experience (e.g., industry experience, research, or advanced technical expertise)</li>
<li>Strong skills in designing, executing, and documenting machine learning experiments</li>
<li>Practical experience with modern generative models</li>
<li>Strong software engineering skills, in particular for data processing, evaluation of ML models, compute cluster orchestration</li>
<li>Experience with large-scale model training, foundation models, model parallelism, multi-node training</li>
<li>Experience with bio sequence data and datasets — various genomic and protein data, sequencing, functional assays, etc</li>
<li>Knowledge of biochemistry, molecular/cell biology, and drug development</li>
</ul>
Compensation
$200K – $275K + Bonus + Equity
 <div>What we offer
<ul>
<li>A competitive compensation package</li>
<li>30 days paid vacation per year</li>
<li>Comprehensive health insurance for US based Beginners</li>
<li>401K with company match for US based Beginners and Direktversicherung for German Beginners</li>
<li>Quarterly company-wide retreats</li>
<li>Monthly wellness benefit</li>
<li>Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland</li>
<li>Learning &amp; Development budget to attend conferences, take courses, or otherwise invest in your professional growth</li>
<li>A buddy to help you get settled</li>
</ul>
*Varies by country and does not apply to internships
At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.
We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.
It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment.
Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.</div>

Inceptive

Open jobs at Inceptive

Data selection and quality evaluation for biological foundation models

Computational design of biological experiments for model development

Foundation and generative models for biomolecules

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