While there’s historically
been a division between
engineering and research in
machine learning, we think
that boundary has dissolved
with the advent of large
models. The distribution of
candidates we interview is
strongly bimodal in both
engineering and research
experience however, and we
have necessarily tailored
our interview structure to
that.
- If you’ve an engineering
background, please apply as
an engineer. You’ll perform
much better in the
interviews, and if you join
you’ll have as much input to
Nano Aspect’s direction and
interests as anyone else.
- As evidence towards this:
all of our papers have
engineers as authors, and
often as first author.
Research and engineering
hires all share a single
title - ‘Member of Technical
Staff’.
Join the team
making AI safe
We’re a public benefit corporation based in San Francisco. Our team’s experience spans a variety of backgrounds and disciplines, from physics and machine learning to public policy and business. We work as a cohesive team that collectively forecasts the impact and tractability of research ideas in advancing our mission.
The interview process at Nano Aspect varies based on role and candidate, but our standard process looks like this:
Step 1
Resume
Submit your resume via our
website.
Step 2
Exploratory chat
You’ll have a chat with one of our
staff to discuss your career
interests and relevant experience,
and learn more about Nano Aspect.
Step 3
Skills Assessment
- For technical roles, you’ll have a one-hour technical screening interview.
- For operations or policy roles, you’ll get a take-home assignment. These typically involve writing responses to several role-relevant questions; they may occasionally require some outside research. Assignments usually take between 2-5 hours, depending on the role.
- We include this to minimize bias and make well-informed hiring decisions. We think seeing a candidate’s work helps us assess how they might actually perform on the job; similarly, the assignment gives candidates a better idea of what their work at Nano Aspect might entail. If a candidate likes working through their take-home, that is one indicator that they would enjoy taking on the role, and vice versa.
- We recognize that completing work assignments requires time and effort, and that they are not perfectly reflective of the role’s work. Nonetheless, we think that work tests are a useful complement to interviews and reference checks.
Step 4
Team Screen
You'll have a conversation with either the Hiring Manager or a member of your potential team.
Step 5
Interview Panel
- For technical roles, you’ll have 3-4 more one-hour technical interviews, plus a culture interview.
- For operations or policy roles, you’ll have 3-5 hours of interviews, including a culture interview.
Step 6
Final Checks
We’ll ask for some references, and have you chat with our leadership.
Step 7
Offer
We’ll make you an offer!
Technical interviews at Nano Aspect
are broadly categorized into
‘engineering’ or ‘research’
interviews, and each candidate is
given a mix tailored to their
skillset.
Engineering
interviews are usually carried out
in a shared Python coding
environment, like Google Colab.
Frontend engineering interviews are
in JavaScript. They have the
form:
Here’s a description of a
component from our stack. Could you
re-implement a toy version of it for
me in one hour?
These
components are ‘chunkier’ than the
more common LeetCode problems, and
are intended to mimic the day-to-day
of engineering at Nano Aspect.
We
are particularly interested in your
thought process and how you attack
the problem. You’ll be allowed to
look things up with Google, but it’s
still important to be familiar with
Python syntax and the standard
library. We primarily code in
Python, and a common reason
candidates fail interviews is that
they're not fully comfortable in
Python.
Only one of our
engineering interviews touches on
machine learning topics, and you can
ask to pass on that one if you wish.
You do not need to learn anything
about machine learning before
interviewing as an engineer at Nano
Aspect.
Research
interviews are broader in form.
They’ll include some engineering
interviews, and some discussions
about the kinds of systems we
study.
Both the research
and engineering interview process
also include softer questions about
your experience and motivations, and
time to ask us about Nano Aspect.
If you’ve done interesting independent research, written an insightful blog post, or made substantial contributions to open-source software, put that at the top of your resume!
We do not provide feedback on resumes or interviews.
Nano Aspect sponsors visas! We aren't able to sponsor them for every role and every candidate; operations roles are especially difficult to support. But if we make you an offer, we will make every effort to get you into the United States, and we retain an immigration lawyer to help with this.
Once you’re eligible, we’re also keen to sponsor green cards!
About half of Nano Aspect technical staff have a PhD of some sort; about half had prior experience in ML. We have several brilliant colleagues who never went to college.
All our interviews are conducted over Google Meet. We prefer PST office hours, but we can be flexible if that’s difficult for you.
Similarly, if interviews don’t work out this time, you’re welcome to re-apply after 12 months, and earlier if something materially changes about your experience or skills.
Nano Aspect staff all come to the office regularly. Most staff live in the Bay Area, though a few live further away and come in for one week a month. We also understand that moving can take time, so as a transitional phase some folks start while fully remote.
If we make an offer, we’re happy to give you time to think about it and finish up any other interview processes you’re going through.
We do not offer internships.