<h2><strong>Join</strong> the team</h2>
<p><strong>Take on a grand challenge:</strong> We’re building a biological systems simulator that will transform how medicines are developed and prescribed. You’ll work on some of the hardest problems researchers, scientists, engineers and designers face. We’re massively motivated by this challenge and are looking for talented folks who are already curious about the domain to join us. <strong>Syntensor offers an opportunity to advance your field while making an outsize impact on the wellbeing of millions of patients.</strong></p>
<p><strong>We are an early stage startup and distributed-first team</strong> working a-synchronously across timezones in the US and UK with significant autonomy and responsibility. <strong>All open roles offer a competitive salary, stock options and 401K (in the US).</strong></p>

<p><strong>We are hiring an innovative computational biologist to develop systems biology approaches within the Syntensor platform, a collaborative, self-service, cloud-based platform that leverages predictive models and complex data to explain drug efficacy and toxicity. Fully remote.</strong>
We are looking for a systems biologist who can improve upon the existing scientific capabilities of our platform in the areas of mechanism of action deconvolution, visual pathway exploration, and biomarker identification.</p>
<h3>The role</h3>
<ul>
<li>Implement omics-based methods to improve our mechanism of action deconvolution algorithms and provide testable hypotheses explaining drug efficacy and toxicity</li>
<li>Develop approaches to network inference to create cell- and tissue-specific signaling networks</li>
<li>Improve the pathway data in the Syntensor graph and collaborate with the product team to implement novel pathway and network visualizations in the user-facing app</li>
<li>Leverage systems biology approaches to identify predictive biomarkers that characterize drug response</li>
<li>Collaborate with bioinformatics scientists, machine learning researchers and software developers to understand requirements for data assets in the Syntensor platform</li>
<li>Competitive compensation - A starting salary in the top quartile for role and level, based on local benchmarks</li>
<li>Stock options - you are joining an early-stage startup we want you to have ‘skin in the game’ and your options package will reflect this</li>
<li>Self-manage - we are a distributed-first company, working from home and collaborating asynchronously from Seattle, NY, Vermont, and Manchester</li>
<li>Take on a grand challenge and share our purposeful mission - we’re building a biological systems simulator that will transform how medicines are developed - you will be making it accessible and powerful for scientists and doctors, with a big downstream impact on the wellbeing of millions of patients</li>
</ul>
<h3>About you</h3>
<ul>
<li>Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related field.</li>
<li>Minimum of 1-2 years of post-PhD experience in bioinformatics with a focus on leveraging multiomics and biological network data in developing systems biology approaches to characterize the cellular state in disease or following chemical/genetic perturbation</li>
<li>Experience with programming in Python</li>
<li>Extensive hands-on experience with high-throughput sequencing data, including whole-genome, exome, and RNA-seq.</li>
<li>Strong domain expertise in systems biology datasets such as interactome datasets and pathway databases, like Reactome</li>
<li>Familiarity with publicly available omics datasets and repositories such as TCGA</li>
</ul>
<h4>Nice to haves</h4>
<ul>
<li>API development in python</li>
<li>Experience applying for access to datasets in dbGaP</li>
<li>Familiarity with the drug discovery and development process</li>
</ul>
<h3>About us</h3>
<h4>Our mission</h4>
<p><strong>We are on a mission to provide access to more effective medicines for millions of patients. We’re building a model of human molecular physiology for research scientists and clinicians that can answer the fundamental question, "will it work?"</strong></p>
<p>Every modernized field of engineering has a systems simulator to test complex interactions in bits rather than atoms. This doesn’t yet exist for biology. Without one, drug development is expensive because risk of failure is very high; 30-60% of prescribed medicines have no clinical benefit to patients and adverse reaction to treatment is the 4th leading cause of death in the US, ahead of pulmonary disease and diabetes. Syntensor is taking on this grand challenge, developing fundamental machine learning methods and applying them at scale to biological data so every individual patient can be prescribed the most effective, least toxic treatment possible.</p>
<h4>What we do</h4>
<p>We are productionizing and scaling up a generalizable machine learning platform that predicts efficacy and toxicity for any drug in any indication. We are using an extensive, heterogeneous biomedical graph, novel fundamental ML methods and advanced engineering infrastructure to generate and explain model outputs for users of our app. Currently, our users are research scientists involved in drug development.</p>
<h4>The team</h4>
<p>We are a small team of people with diverse skills and a shared bias towards problem solving and execution. We are inventors and builders who believe in the scientific method; feedback and iteration is essential to our process and we share our work early and often. That said, we aim high. Our mission and the domain in which we operate demand that we take on some of the hardest problems researchers, scientists, engineers and designers face, and we are determined to build technology that solves them properly and usefully for users of our platform. We are looking for talented people who are motivated by the challenge of hard problems and who are already curious about the technological, scientific or cultural domains with which we engage.</p>
<p>We are a distributed-first team and very relaxed about where and when work happens, but come together as a whole team weekly to sync-up. We work with intrinsic curiosity and motivation towards well defined goals (even where there are unknown unknowns). Our diversity, great communication and respectful, supportive teamwork make us highly effective.</p>

Computational Systems Biologist

<p><strong>We are hiring a lead engineer to co-own the technical execution of a collaborative, self-service, cloud-based platform that leverages predictive models and complex data to explain drug efficacy and toxicity. Fully remote.</strong></p>
<h3>The role</h3>
<ul>
<li>Lead the engineering of the Syntensor platform, a first-of-its-kind collaborative, self-service platform for improving the efficiency of the drug discovery process</li>
<li>Prepare our platform to scale; iterate on and improve the platform’s architecture and its associated services and databases</li>
<li>Develop data visualization approaches for multimodal biomedical data, with a focus on pathway and network visualization</li>
<li>Own the development of the Syntensor user-facing app, a large React application</li>
<li>Architect and develop the app backend, presently developed with Node.js on AWS</li>
<li>Define and build strong foundations to your own high standards - CI/CD processes, devops, monitoring and scaling, and security</li>
<li>Collaborate closely with the head of product to determine the technical design of new features in the user-facing product</li>
<li>Work with a small hands-on team - you’ll work with machine learning researchers to shape overall system architecture and integrations with ML systems (Python/Pytorch, Geometric Deep Learning)</li>
<li>Make fundamental business impact - you will take an active role in shaping our product, creating a strong engineering culture and building a highly skilled and focused team</li>
<li>Competitive compensation - A starting salary in the top quartile for role and level, based on local benchmarks</li>
<li>Stock options - you are joining an early-stage startup we want you to have ‘skin in the game’ and your options package will reflect this</li>
<li>Self-manage - we are a distributed-first company, working from home and collaborating asynchronously from Seattle, NY, Vermont, and Manchester</li>
<li>Take on a grand challenge and share our purposeful mission - we’re building a biological systems simulator that will transform how medicines are developed - you will be making it accessible and powerful for scientists and doctors, with a big downstream impact on the wellbeing of millions of patients</li>
</ul>
<h3>About you</h3>
<ul>
<li>Deep knowledge of Javascript and Typescript</li>
<li>Extensive experience developing large React applications</li>
<li>Experience designing and building reliable REST APIs</li>
<li>Experience with modern JS tooling and build systems</li>
<li>Working knowledge of authentication, authorization and related security practices</li>
<li>Experience designing data schemas and managing relation and non-relational databases (we use MySQL and ElasticSearch)</li>
<li>Working knowledge of Python for data processing and API development</li>
<li>Good knowledge of CSS and responsive design (we use CSS modules)</li>
<li>Experience architecting and maintaining cloud infrastructure (we use AWS)</li>
<li>Experience with Git, Docker and CI/CD pipelines</li>
<li>Experience with asynchronous work within a remote team across multiple time zones</li>
<li>Ability to self-manage in a small team environment</li>
<li>English fluency, verbal and written</li>
</ul>
<h4>Nice to haves</h4>
<ul>
<li>Familiarity with machine learning concepts and applications</li>
<li>Knowledge of data visualization libraries such as D3.js or Chart.js</li>
<li>Experience with unit and E2E testing (we use Cypress)</li>
<li>Knowledge of modern authorization mechanisms, such as JSON Web Token</li>
<li>Experience architecturing large cloud-based systems using microservices and serveless paradigm</li>
<li>Experience designing pipelines and storage solutions for ML-driven systems with large volume of complex data</li>
<li>Experience managing infrastructure using code-as-infrastructure paradigm - we use AWS CDK</li>
<li>Experience with integration of 3rd party corporate systems or experience with work in regulated corporate settings (e.g. healthcare or finance)</li>
<li>Experience interfacing with ML systems (ours is Python/Pytorch)</li>
<li>Experience developing highly interactive visualizations of complex data</li>
<li>Good eye for UI and knowledge best of UX principles</li>
<li>Experience with Figma</li>
<li>Experience with biomedical data and bioinformatics</li>
<li>Experience managing and growing engineering teams</li>
<li>Experience with WebGPU</li>
</ul>
<h3>About us</h3>
<h4>Our mission</h4>
<p><strong>We are on a mission to provide access to more effective medicines for millions of patients. We’re building a model of human molecular physiology for research scientists and clinicians that can answer the fundamental question, "will it work?"</strong></p>
<p>Every modernized field of engineering has a systems simulator to test complex interactions in bits rather than atoms. This doesn’t yet exist for biology. Without one, drug development is expensive because risk of failure is very high; 30-60% of prescribed medicines have no clinical benefit to patients and adverse reaction to treatment is the 4th leading cause of death in the US, ahead of pulmonary disease and diabetes. Syntensor is taking on this grand challenge, developing fundamental machine learning methods and applying them at scale to biological data so every individual patient can be prescribed the most effective, least toxic treatment possible.</p>
<h4>What we do</h4>
<p>We are productionizing and scaling up a generalizable machine learning platform that predicts efficacy and toxicity for any drug in any indication. We are using an extensive, heterogeneous biomedical graph, novel fundamental ML methods and advanced engineering infrastructure to generate and explain model outputs for users of our app. Currently, our users are research scientists involved in drug development.</p>
<h4>The team</h4>
<p>We are a small team of people with diverse skills and a shared bias towards problem solving and execution. We are inventors and builders who believe in the scientific method; feedback and iteration is essential to our process and we share our work early and often. That said, we aim high. Our mission and the domain in which we operate demand that we take on some of the hardest problems researchers, scientists, engineers and designers face, and we are determined to build technology that solves them properly and usefully for users of our platform. We are looking for talented people who are motivated by the challenge of hard problems and who are already curious about the technological, scientific or cultural domains with which we engage.</p>
<p>We are a distributed-first team and very relaxed about where and when work happens, but come together as a whole team weekly to sync-up. We work with intrinsic curiosity and motivation towards well defined goals (even where there are unknown unknowns). Our diversity, great communication and respectful, supportive teamwork make us highly effective.</p>

Lead Full-stack Engineer

<p><strong>We are hiring an ML research engineer to interface large-scale representation learning methods with experimental genomic and transcriptomic datasets. Fully remote.</strong></p>
<p><strong>Join a team of world-leading experts in this domain</strong> and help drive the field forward as we tackle a problem space only recently made tractable by advances in ML and HPC.</p>
<h3>The role</h3>
<ul>
<li>Use your expertise to develop foundational models based on biological and biochemical data corpuses for applications in early stage drug discovery</li>
<li>Productionalize research code and add features to state of the art models</li>
<li>Implement and train baseline models for downstream tasks</li>
<li>Identify, implement, and maintain model performance metrics and indicators</li>
<li>Lead and/or contribute to publications in top-tier journals</li>
<li>Stay up to date with relevant research and industry trends for integration into our products</li>
<li>Take on a grand challenge and share our purposeful mission - we’re building a biological systems simulator that will transform how medicines are developed - you will be making it accessible and powerful for scientists and doctors, with a big downstream impact on the wellbeing of millions of patients</li>
<li>Competitive compensation - A starting salary in the top quartile for role and level, based on local benchmarks</li>
<li>Stock options - you are joining an early-stage startup we want you to have ‘skin in the game’ and your options package will reflect this</li>
<li>Self-manage - we are a distributed-first company, working from home and collaborating asynchronously from Seattle, NY, Vermont, and Manchester</li>
</ul>
<h3>About you</h3>
<ul>
<li>Masters in Computer Science, Engineering, or other STEM field and 4+ years of experience in applied machine learning and software engineering</li>
<li>PhD in Computer Science, Engineering, or other STEM field and 2+ years of experience in applied machine learning and software engineering</li>
<li>Knowledge of statistics for scientific data (e.g. Design of Experiments)</li>
<li>Experience working within the domain of biological systems</li>
<li>Experienced with large scale representation learning</li>
<li>Experienced with long context sequence models</li>
<li>Experience with tabular data</li>
<li>Experience with ML + data science libraries such as PyTorch, Pandas, Scikit-learn etc.</li>
<li>Proficient data engineering skills (e.g. stats, data wrangling, feature engineering)</li>
<li>Python Expertise</li>
<li>Familiarity with cloud platforms like AWS</li>
<li>Understanding of version control (Git) and software engineering best practices</li>
<li>Experience with early-stage startups</li>
</ul>
<h3>About us</h3>
<h4>Our mission</h4>
<p><strong>We are on a mission to provide access to more effective medicines for millions of patients. We’re building a model of human molecular physiology for research scientists and clinicians that can answer the fundamental question, "will it work?"</strong></p>
<p>Every modernized field of engineering has a systems simulator to test complex interactions in bits rather than atoms. This doesn’t yet exist for biology. Without one, drug development is expensive because risk of failure is very high; 30-60% of prescribed medicines have no clinical benefit to patients and adverse reaction to treatment is the 4th leading cause of death in the US, ahead of pulmonary disease and diabetes. Syntensor is taking on this grand challenge, developing fundamental machine learning methods and applying them at scale to biological data so every individual patient can be prescribed the most effective, least toxic treatment possible.</p>
<h4>What we do</h4>
<p>We are productionizing and scaling up a generalizable machine learning platform that predicts efficacy and toxicity for any drug in any indication. We are using an extensive, heterogeneous biomedical graph, novel fundamental ML methods and advanced engineering infrastructure to generate and explain model outputs for users of our app. Currently, our users are research scientists involved in drug development.</p>
<h4>The team</h4>
<p>We are a small team of people with diverse skills and a shared bias towards problem solving and execution. We are inventors and builders who believe in the scientific method; feedback and iteration is essential to our process and we share our work early and often. That said, we aim high. Our mission and the domain in which we operate demand that we take on some of the hardest problems researchers, scientists, engineers and designers face, and we are determined to build technology that solves them properly and usefully for users of our platform. We are looking for talented people who are motivated by the challenge of hard problems and who are already curious about the technological, scientific or cultural domains with which we engage.</p>
<p>We are a distributed-first team and very relaxed about where and when work happens, but come together as a whole team weekly to sync-up. We work with intrinsic curiosity and motivation towards well defined goals (even where there are unknown unknowns). Our diversity, great communication and respectful, supportive teamwork make us highly effective.</p>

Machine Learning Research Engineer

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Syntensor

Join the team

Computational Systems Biologist

Multiomics | Systems biology | Causal reasoning | Network inference | Precision Medicine | Mechanism of action deconvolution | Machine learning | Product development

Fully remote | Top quartile salary | Stock options | 401K | Health, dental and vision insurance

Lead Full-stack Engineer

Javascript | Typescript | React | CSS | Node | AWS | Graph databases | Relational and non-relational databases | Data Visualization | Product development

Fully remote | Top quartile salary | Stock options | 401K | Health, dental and vision insurance

Machine Learning Research Engineer

Representation Learning | Long Context Sequence Modeling | Tabular Data | Statistics | Software Engineering | Biological Systems | Genomics

Fully remote | Top quartile salary | Stock options | 401K | Health, dental and vision insurance