Join Signaloid: Applied Mathematician (Probability, Measure Theory, and Statistics) Role

Signaloid offers a groundbreaking computing platform designed to dynamically track data uncertainties throughout executions. Leveraging deterministic computations on in-processor representations of probability distributions, our platform supersedes traditional Monte Carlo methods by providing substantial speedups and cost reductions. Available as a cloud-based engine with a task execution API, Signaloid also supports on-premises and edge-hardware implementations, ensuring seamless operation even without a cloud connection.

Why Choose Signaloid?

Our platform stands out as the most cost-effective and fastest solution for engineering uncertainty quantification applications. Whether it's options pricing and portfolio modeling in finance or materials modeling and photonics simulation in engineering, our system outpaces Monte Carlo-based implementations running on high-end AWS EC2 instances by an order of magnitude or more.

Our Team

Join a team of contrarian engineers with extensive research, engineering, and leadership experience from industry giants like Apple, ARM, and Bell Labs, as well as prestigious institutions such as CMU, Cambridge, MIT, and Oxford. Learn more and try the Signaloid uncertainty-tracking computing platform by signing up for free at .

Role Description

As an Applied Mathematician, you'll collaborate closely with Signaloid's founder and engineering teams. Your mission: develop new mathematical techniques to enhance our platform's ability to perform deterministic computations on finite-dimensional in-processor representations of probability distributions. This role prioritizes applied research within product-centric deliverables and deadlines.

Key Responsibilities

• Create new variants of finite-dimensional probability distribution representations.
• Work with fellow mathematicians to explore properties of these representations.
• Develop robust C/C++ implementations, ensuring they are well-tested, documented, and product-ready.
• Extend and create analytical bounds and proofs for distribution representations.
• Examine the impact of distribution representations across various domains, from machine learning to finance.
• Communicate findings through internal documentation, public research publications, and blog posts.

Growth Opportunities

Within a year, expect to take on more responsibility in shaping the future of applied probability theory and statistics on our platform, liaise with researchers, and expand into areas showcasing your exceptional skills.

Requirements

Minimum Skills and Experience

• Master's degree or PhD in applied mathematics or a related discipline.
• Strong background in probability, measure theory, and statistics.
• Proven research and publications in applied mathematics, sciences, or engineering.
• Exceptional analytical abilities and quick learning capacity.
• Experience programming in C/C++.
• Ability to communicate ideas clearly to non-mathematicians and work across engineering functions.
• Honesty, empathy, and a keen understanding of diverse perspectives.

Desirable Skills and Experience

• Strong background in numerical linear algebra.
• Experience with stochastic differential equations.
• Understanding of uncertainty in measurements and engineered systems.
• Familiarity with Python.

Our Recruiting Procedure

We prioritize clear communication skills in a remote working environment. Submit a one-page cover letter or a creative code snippet that works on our platform. Successful applicants will undergo a multi-stage interview process including a Zoom call, a coding project, and up to six interviews with core team members.

Benefits

• Flexible remote-first work environment.
• International team collaboration with periodic in-person sessions.
• Competitive compensation with yearly and bi-yearly bonuses.
• Transparent pay structure with defined skill levels.
• Attractive stock options.
• Respectful, driven work culture with strong team collaboration.

Join us in challenging the status quo and engineering the future of uncertainty quantification. Apply now to become part of the innovative team at Signaloid.