Careers

If you would like to be a part of our team, please email us with your interest: xy [at] sl-machines [dot] com

You can share with us:

1. 2-3 bullet points on your most significant technical projects. We’re for the most part interested in learning about you and some of the hardest technical problems you've worked on, and how you’ve approached these problems.
2. How you learned about us, and why you’re interested in being part of the research, engineering, and capabilities we’re working on.
3. Any of your preferred links. We’d like to learn more about you and your work (research papers, thesis or dissertation, patents, GitHub, portfolio, architecture specifications, technical writeups, blog, talks, works-in-progress, or your most-recent CV) — anything that allows us to interact directly with the character of your work.

We have a thesis-defined approach to capability design. As part of this, we are focused on finding strong researchers and engineers in discrete mathematics, computer architecture, applied physics and nanofabrication, hardware-software codesign, high-reliability machining, advanced materials, medical physics, and advanced integration.

We offer members of our team a strong compensation + equity package, generous benefits, and a serious setting for working on and having an early, enduring influence on important problems across device architecture, applied mathematics, physics and computation, and frontier applications.

We hold durable IP foundations across our architectures and are working toward accelerated timelines with near-term deployment targets.

We are also taking significant ownership over our critical-path architecture capabilities. This means interfacing at both short-range and long-range intensities with abstruse problem sets within our technology, as well as with otherwise-introduced constraints that are concomitant to doing pure approaches and system design for high-performance ultra-precision devices.

We like to go the distance. We think of this as having facility for abstraction, in either or a combination of these ways:

• Basal acuity • A generally fair capacity for continuous, polyglottal pattern recognition; the ability to adapt information sparsity and manage for tradeoffs at different structural levels; a kind of stubborn, x-ray temperament for peering through noise keenly.
• Higher-order awareness • A sort of proclivity for deconstructing the make-up of a complex structure so that it can be more meaningfully approached (engineered) from the outside; some disposition for switching across, or between, abstraction layers.
• Staying intuition • We think anomalies are high-information indicators of hidden or latent structure; finding them often exposes previously inaccessible, structurally rich regions of both theory and utility space. For this latter kind of space, there are productive methods of instinct and pseudorandomness that we like to apply heuristically in research, design, and application.

We maintain tightly closed feedback loops between our R&D priorities, and focus on efficient velocity and implementation between our systems interests. This translates to how we focus on effectively structuring and carrying out the hard-input operating models necessary for executing each of our architectures within specified timelines and parameters.

We think of our research, engineering, and company operating principles along these dimensions: