<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 East and West Coast US, Canada and Europe with significant autonomy and responsibility. <strong>All open roles offer a competitive salary, stock options and 401K.</strong></p>

<p><strong>Geometric machine learning provides a unique mechanism for understanding the dynamics of biological systems through the lens of dynamical systems theory.</strong> Syntensor maintains a single backbone model which is used to capture the dynamics and state-space of a very large-scale representation (2m nodes per cell type for ~2k cell types) of human physiology, and neural Differential Equation-based approaches  (in combination with Graph Neural Networks) are at the core of this effort.</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. <strong>Work on edge-of-chaos dynamics, multiple-attractor states, stability and control mechanics, and more, all at large (computational) scale with models scaling to 100s of millions of parameters.</strong></p>
<h3>The role</h3>
<p><strong>This role offers the potential for extraordinary impact in a well-established market/economy for deep learning applications.</strong></p>
<ul>
<li>Find and implement recently published methods with the goal of inventing new architectures and methods for dynamical systems simulation in biology. To do this you will develop methodology and experiments to validate implementations and possible improvements by testing on smaller, academic problems.</li>
<li>Get full support with publishing results (using toy/academic datasets) if effective methods are discovered.</li>
<li>OSS contributions - Improve the Torchdyn library by integrating existing methods such as those from Julia’s DE Packages, Diffrax and TorchDiffEq and fill in missing gaps in the DiffEqML ecosystem.</li>
<li>Collaborate with DiffEqML, MILA, UM, Stanford and other academic teams and get exposure to leading methods in deep learning and HPC.</li>
<li>Competitive compensation: We offer 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>401K for US-based team members.</li>
<li>Your health, dental and vision insurance will be fully covered, with partial coverage for family/partners as well.</li>
<li>Self-manage: Enjoy the flexibility and opportunities that come with early stage startups. You’ll work closely with all areas of the business with a high degree of autonomy and the opportunity to progress as an individual contributor or manager. Syntensor is a distributed-first company, working from home and collaborating asynchronously from the East and West Coast US, Canada and Europe.</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 and prescribed. You will be advancing the field while making a big impact on the wellbeing of millions of patients.</li>
</ul>
<h3>About you</h3>
<ul>
<li>3 years experience at graduate+ level in Differential Equations (coursework/research in numerical methods, Ordinary Differential Equations, Partial Differential Equations, Nonlinear Dynamical Systems)</li>
<li>Strong theoretical understanding of Dynamical Systems</li>
<li>Publications in computational biology/physics/mathematics/chemistry or publications in ML</li>
<li>Familiar with graphs, topology, geometric modeling</li>
</ul>
<h4>Nice to haves</h4>
<ul>
<li>Open Source Software contributor</li>
<li>Publication(s) in applications of Deep Learning towards computational biology/physics/mathematics/chemistry</li>
<li>Successfully invented and implemented a unique use-case for applying ML + differential equations</li>
<li>Experience working within the domain of biological systems</li>
<li>PyTorch / Julia / JAX User</li>
<li>Publications at a premier CS conference e.g. ICML, NeurIPS, ICLR, etc.</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>

ML Researcher - Differential Equations

<p><strong>Geometric machine learning provides a unique mechanism for understanding the dynamics of biological systems through the topology of those systems.</strong> Syntensor maintains a single backbone model which is used to capture the dynamics and state-space of a very large-scale representation (2m nodes per cell type for ~2k cell types) of human physiology, and Graph Neural Networks are at the core of this effort.</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>
<p><strong>This role offers the potential for extraordinary impact in a well-established market/economy for deep learning applications.</strong></p>
<ul>
<li>Invent, benchmark, and implement state-of-the-art methods in Pytorch Geometric (PyG). To do this you will develop methodology and experiments to validate implementations and possible improvements by testing on smaller, academic problems.</li>
<li>Get full support with publishing results (using toy/academic datasets) if effective methods are discovered.</li>
<li>Contribute to various Open Source projects e.g. PyG, TorchDyn, Pytorch Lightning, PyTorch, Julia, JAX.</li>
<li>Collaborate with DiffEqML, MILA, UM, Stanford and other academic teams and get exposure to leading methods in deep learning and HPC.</li>
<li>Competitive compensation: We offer 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>401K for US-based team members.</li>
<li>Your health, dental and vision insurance will be fully covered, with partial coverage for family/partners as well.</li>
<li>Self-manage: Enjoy the flexibility and opportunities that come with early stage startups. You’ll work closely with all areas of the business with a high degree of autonomy and the opportunity to progress as an individual contributor or manager. Syntensor is a distributed-first company, working from home and collaborating asynchronously from the East and West Coast US, Canada and Europe.</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 and prescribed. You will be advancing the field while making a big impact on the wellbeing of millions of patients.</li>
</ul>
<p>### About you</p>
<ul>
<li>3 years experience at graduate+ level in GNNs</li>
<li>Strong theoretical understanding of graphs, topology, geometric modeling</li>
<li>Publications in ML or, even better, ML + Hard Science</li>
<li>Familiar with dynamical systems and the applications of differential equations towards time-series modeling</li>
</ul>
<h4>Nice to haves</h4>
<ul>
<li>Experience working within the domain of biological systems</li>
<li>OSS contributor</li>
<li>Successfully invented and implemented a unique use-case for a GNN</li>
<li>PyTorch Geometric / DGL / JAX user</li>
<li>Publications at a premier CS conference e.g. ICML, NeurIPS, ICLR, etc.</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>

ML Researcher - Graph Neural Networks

<p>Syntensor may use cookies to ensure that you get the best possible experience of our website and web app. <a href="/privacy/cookies">Cookie Policy</a></p>

Functional cookies remember preferences in order to provide the best possible user experience. For example, we will recognize your username and apply any content preferences you provided.

Functional cookies

Analytics cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously. For example, we use Matomo Analytics to view anonymous, aggregated data about visitors to our website.

Analytical cookies

Matomo enables website owners to track anonymous data without using cookies to improve user experience. For example, we can understand which pages are most popular and what percentage of users completes a form.

Cookie-less tracking

Necessary cookies are permitted by law to make website features usable. For example, these cookies let us recognize that you have created an account and have logged in.

Syntensor

Join the team

ML Researcher - Differential Equations

Geometric machine learning | Graphs | Topology | Geometic modeling | Neural DEs | Biological systems | Scale to 100s of millions of parameters | OSS Contribution | Collaboration with leaders in the field

Fully remote: We hire the right people wherever they are | Top quartile salary | Stock options | 401K | Health, dental and vision insurance | OSS contribution | Collaboration with leaders in the field

ML Researcher - Graph Neural Networks

Geometric machine learning | Graph Neural Networks | Topology | Biological Systems | Dynamical Systems | Drug Discovery | OSS Contribution | Biopharma

Fully remote: We hire the right people wherever they are | Top quartile salary | Stock options | 401K | Health, dental and vision insurance | OSS contribution | Collaboration with leaders in the field