Signaloid provides a computing platform that tracks data uncertainties dynamically and throughout computations in execution workloads. Our computing platform uses deterministic computations on in-processor representations of probability distributions, to enable orders of magnitude speedup and lower implementation cost for computing tasks traditionally solved using Monte Carlo methods. The platform is available as a cloud-based computing engine that lets you run tasks via a cloud-based task execution API. We also provide on-premises and edge-hardware implementations of our computing platform for customers who want to use their existing on-site infrastructure and for use cases requiring operation without connection to the cloud.

Our platform is the most cost-effective way to engineer uncertainty quantification applications and is also the fastest way to run uncertainty quantification tasks, for key use cases. Workloads ranging from options pricing and portfolio modeling in finance, to uncertainty quantification for materials modeling and photonics simulation in engineering, often run an order of magnitude or more faster, compared to Monte-Carlo-based implementations running on high-end AWS EC2 instances.

Our team consists of contrarian engineers with combined research, engineering, and leadership experience from Apple, ARM, Bell Labs, CMU, University of Cambridge, IBM Research, MIT, NEC Labs, and University of Oxford. Find out more and try out the Signaloid uncertainty-tracking computing platform by signing up for free for our developer platform, at https://get.signaloid.io.

Role Description

As a successful candidate in this role, you will work closely with Signaloid's founder and with Signaloid's engineering teams. In doing so, you will develop new mathematical techniques to underpin the Signaloid compute platform's capability for performing deterministic computation on finite-dimensional in-processor representations of arbitrary probability distributions. This role is an applied research position and you will work within the constraints of product-led deliverables and deadlines.

During your first year in this role, you will:

• Develop new variants of finite-dimensional representations of probability distributions for use in Signaloid's compute platform.
• Work collaboratively with other applied mathematicians at Signaloid to investigate properties of existing and new finite-dimensional representations of probability distributions for use in Signaloid's compute platform.
• Grow into the ability to develop robust C/C++ implementations of your distribution representations within the frameworks of Signaloid's engineering infrastructure and test, document, and package them for integration into products.
• Take on feedback from the engineering teams and ensure your outputs are robust and efficient enough for direct incorporation into products.
• Extend existing analytic bounds and proofs, as well as develop new bounds and proofs, of properties of the distribution representations underlying Signaloid's compute platform.
• Explore the impact of the distribution representations across a wide range of application domains, ranging from machine learning to stochastic differential equations in finance applications, using algorithms implemented on Signaloid's compute platform.
• Communicate the results of your activities, on a regular schedule, through internal documentation, communication with internal stakeholders, as well as in public research publications and blog posts.

Within a year in this role, you can expect to:

• Take on more responsibility in contributing to the direction of applied probability theory and statistics, of Signaloid's platform.
• Contribute to liaising with researchers and advanced R&D organizations employing Signaloid's compute platform.
• Expand your role to encompass other areas in which you have demonstrated exceptional competence.