Engineer, New Grad

Spectral Labs; San Francisco (CA); On-site; USD 150,000 - 220,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

401(k) with Company match

Company-expensed meals

Commuting covered

Job summary

Spectral Labs is hiring new graduates for a broad, hands-on role that spans machine learning, geometry, graphics, and AI. You’ll join a small team, contribute across the stack, and quickly learn which area you enjoy most by working on real problems.

The position is a fully in-person, full-time role at our SF office. A strong self-starter with excellent problem-solving will thrive here, with mentorship and ownership from day one.

Qualifications

• Real strength in at least one of machine learning, geometry, graphics, or AI, and curiosity about others.
• Strong programming ability with Python and/or C/C++.
• Comfort with Linux, git, CUDA, and cloud infrastructure.

Responsibilities

• Work across ML, geometry, graphics, and AI tasks.
• Build and maintain geometry/data pipelines and tooling.
• Develop evaluation harnesses and support training runs.
• Create internal tools to reduce friction for the team.
• Take on scoped research/engineering tasks and expand as you prove yourself.

Skills

Python

C/C++

Linux

Git

CUDA

Cloud infrastructure

Education

Bachelor’s or Master’s in CS/Engineering/Math/Physics

Job description

What We're Looking For

Spectral Labs is building foundation models for engineering physical systems. We are hiring new graduates for raw ability, not domain experience. What we look for instead of heavy experience with CAD kernels and generative geometry is evidence that you teach yourself hard things quickly and finish them.

You'll join our technical staff, and the role is deliberately broad. The work sits across machine learning, geometry, graphics, and AI: geometry and data pipelines, evaluation harnesses, internal tooling, and training and experiment support. We're hiring a generalist, a real strength in at least one of those areas and genuine curiosity about the rest. You don't come with a specialty yet, and that's the point. A small team gives you the whole system, and you'll find out where you're strongest by working across it. You'll get real problems early, and the people around you will help whenever you ask, but you'll be the one steering.

This is a fully in-person, full-time role at our office in SF.

What You'd Work On

• The highest-leverage work in the building. This could be:

  • Building and maintaining pieces of our geometry and data pipelines, parsing CAD, converting formats, extracting features, and verifying that what comes out matches what went in,

  • Writing the evaluation harnesses and tooling that tell us whether a model actually got better,

  • Supporting training and experiment runs: instrumenting them, debugging failures, and turning results into something the team can read at a glance,

  • Building internal tools that remove friction for everyone else, the script nobody wanted to write that saves the team a day a week,

  • Taking well-scoped pieces of larger research and engineering efforts and grow your scope as you earn it,

  • Collecting data vital to our moat,

  • And/or fixing things you notice are broken without waiting to be asked.

Qualifications

• Graduating with a Bachelor's or Master's in Computer Science, Engineering, Math, Physics, or a related technical field, or graduated within roughly the last two years. You have a high GPA in a competitive undergrad program.

• Real strength in at least one of machine learning, geometry, graphics, or AI, and genuine curiosity about the others.

• Strong programming ability. You write Python and/or C/C++ comfortably.

• Evidence that you finish hard things nobody assigned you: a substantial personal project, research you drove yourself, open-source contributions, competition results.

• You love solving challenging problems.

• Comfort with a high-ownership environment.

• Objectively impressive achievements in any domain are a big plus. Built something unusual? Won something hard? We want to hear about it.

Big Pluses

• Research experience with a paper, preprint, or thesis you can talk about in depth.

• Work on generative models: diffusion, autoregressive, or otherwise, where you trained something yourself and evaluated it honestly.

• Experience generating synthetic data and showing it moved a real metric.

• Experience building RL environments, reward functions, or post-training pipelines.

• Coursework or projects in computer graphics or computational geometry: renderers, meshes, geometry processing, spatial data structures.

• Any work modeling physical or engineered systems, simulation, control, robotics, motion planning, or physics-informed ML.

• A track record of making things dramatically faster. Profiling, kernels, pipelines, latency: we care about the order of magnitude and how you found it.

• Internship or contract experience where you shipped something real users depended on.

• Competitive programming, math, or physics results (ICPC, IOI, IMO, Putnam, or similar).

• You've read the papers in this space and have opinions about them.

• Comfort with Linux, git, CUDA, and cloud infrastructure.

Visa Sponsorship

We fully sponsor visas end-to-end. We've done it before, we have immigration counsel, and if we want to hire you, we don't want paperwork to get in the way. If we make you an offer, we'll put real resources and effort behind getting you here. The process can be slow, frustrating, and stressful, but we'll handle the heavy lifting on our end so you can focus on what you're great at.

Compensation, Benefits, and Perks

• Salary between $150-220k, in addition to competitive equity and bonuses

• Multiple health insurance options, including option with 100% premium covered

• Dental insurance

• 401(k) with Company match

• Company-expensed lunch, dinner, and snacks at the office

• Company-expensed commuting to and from the office

If you're on the fence about whether you're qualified enough, apply anyway. The problems we're working on benefit from people who come at them from different angles, and we've seen firsthand that the best hires aren't always the most obvious ones on paper. We'd rather see your application and decide together than have you filter yourself out preemptively.