Applied Mathematician (Probability, Measure Theory, and Statistics) (GB)

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.

Requirements

Minimum Required Skills and Experience:

• Masters degree or PhD in applied mathematics or a related discipline.
• Strong background in probability and measure theory and statistics.
• Demonstrable research and publications in applied mathematics, sciences, or engineering.
• Demonstration of exceptional analytical abilities and quick comprehension of new topics.
• Eagerness to work on real-world applications.
• Experience programming in C/C++.
• An ability to communicate ideas to non-mathematicians and eagerness to work with individuals across the spectrum of engineering functions.
• An ability to communicate concepts from diverse fields, succinctly.
• A willingness to listen to people until they feel understood.
• Honesty, empathy, and a willingness to see the world from the viewpoint of others.

Additional Desirable Skills and Experience:

• Strong background in numerical linear algebra.
• Strong background in stochastic differential equations.
• An understanding of the role of uncertainty in measurements and in engineered systems.
• Familiarity with Python.

Our Recruiting Procedure

1. All positions require you to write a brief cover letter that should be no more than one page long. The more concise the better. You can also substitute the cover letter for a snippet of code that will run on the signaloid.io platform; be creative! We use the cover letter / code snippet to screen for communication skills, as clear communication is essential in a remote working environment.
2. Applicants who pass the cover letter screening receive an initial 15-minute Zoom screening call with the CEO/CTO.
3. Applicants who pass the screening interview will be given a coding project that can be solved using the Free Tier of Signaloid's Signaloid Cloud Developer Platform. We will also provide you with additional free credits on the Signaloid Cloud Developer Platform. The coding exercise will be simple enough to complete in a few hours. You will however have a time window of one week or two weeks (your choice) to complete the coding exercise. You are encouraged to make your implementation open source on GitHub.
4. Applicants who successfully complete the coding exercise are invited for a set of interviews with people from our core teams (there will be up to six interviewers). The interviewers will use the project you completed as a discussion point.
5. In the final stage, applicants are invited for an on-site (or "virtual on-site") day with members of the team you are interviewing to join. During this day, we will work with you on a hands-on simulation of a real working day solving a task relevant to the position you are applying for, working with your potential future colleagues.

Benefits

A flexible remote-first work environment

• Be part of an international team with the flexibility to choose where you live, as long as you are available during the working hours of 09:00 to 17:00 UK time.
• Join the rest of the team several times each year for an in-person session somewhere in Europe.

Competitive compensation

• Yearly bonus based on company's Objectives and Key Results (OKR) performance and bi-yearly bonus based on your project team's OKR performance.
• Simple transparent compensation across the company, with four pay levels, in all roles, based on skill level: Contributor, Senior Contributor, Lead Contributor, and Principal Contributor.
• All full-time employees receive attractive stock options package.

A driven but respectful environment

• We never speak ill of others even if we differ in our viewpoints; we show up every day with a sense of urgency; we treat each other with respect as though each day were our last.
• No isolated "projects": No person in the team works in isolation and a successful outcome for the thread of work you lead will inherently depend on getting help from (and helping) other members of the team.