Data Scientist

HiddenLayer

Data Scientist improving LLM-powered systems and security products at HiddenLayer. Building, shipping, and enhancing core models within a focused team for effective security solutions.

Posted 4/27/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

About the role

Key responsibilities & impact

You're looking for a Data Scientist to join our Data Sciences and ML Engineering team. You'll be building, shipping, and improving the models and LLM-powered systems that sit at the core of our security products.
This is a hands-on role on a small, focused team. You'll have real ownership over the models and pipelines you build, close collaboration with engineering and product, and the runway to go deep on the hard problems.
Your work will span a few areas:
Model development and research. Building classifiers, detectors, and scoring models on messy, high-stakes security data. Designing experiments, evaluating trade-offs, and iterating on architectures — not just hyperparameters.
LLM agent systems. Shaping the prompts, context, tool-use patterns, and supporting content that drive our LLM agents.
Production delivery. Shipping models behind real traffic, monitoring them, and improving them over time.
Evaluation and iteration. Building the evaluation harnesses and feedback loops that let us know whether a change is actually an improvement — often the hardest part of the work. Our models only improve for customers when our evaluations highlight what really matters.

Requirements

What you’ll need

Production experience is the single most important thing. We'd like to see around 3–4+ years of experience delivering models into production environments where they've had to perform, be maintained, and evolve. That's the background that tends to set people up for success here.
Depth in ML fundamentals. You understand model architectures and can reason about why a given approach is or isn't a good fit for a problem. You've moved well past treating models as black boxes and past tuning that stops at sample weights and decision thresholds.
Willingness to experiment. You're comfortable trying genuinely novel approaches when the standard playbook runs out, and you can tell the difference between a promising result and a fragile one.
Strong engineering instincts. Your code is something teammates can read, extend, and trust in production. You think about reproducibility, testing, and handoff — not just whether something runs on your laptop.
Experience with LLMs in practice. You've worked with LLM-based systems in some real capacity — prompting, context design, tool use, evaluation, or fine-tuning — and have opinions shaped by actually shipping things.
Comfort with ambiguity. Security problems rarely come with clean labels or clean data. You're able to frame problems, scope them, and make progress without a fully paved path. You’ll help highlight ambiguity and reason about how to make progress even when humans don’t all agree on one single answer.
An advanced degree (MS or PhD) in a technical discipline. This doesn't have to be in data science or ML specifically — strong backgrounds in CS, statistics, physics, math, engineering, and related fields are all welcome. Your on-the-job experience is what matters the most.

Benefits

Comp & perks

Fully Remote: We are a completely remote global team. Though we’re distributed, we are intentional about getting the team together a couple of times a year. We offer a generous stipend for your home office setup, annual upgrades to ensure you have a comfortable workspace and a monthly stipend for internet/phone expenses.
Comprehensive Health & Wellness Benefits: Better than your average startup healthcare benefits. With five options to choose from, of which are fully subsidized by HiddenLayer, we offer a variety of options to fit each person’s needs. We also offer vision, dental, and 401k offerings.
Flexible Time Off: Enjoy unlimited and flexible time off for all salaried employees, in addition to 15 paid company holidays.
Commitment to Learning and Development: We support personal growth and education through a dedicated L&D fund that can be used for training, conferences, certifications and industry events.
Diversity, Equity, and Inclusion: We are committed to building a diverse team with individuals from various backgrounds, experiences, abilities, and perspectives, and we are proud to be an equal opportunity employer.

ATS Keywords

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Hard Skills & Tools

model developmentmachine learning fundamentalsLLM systemsproduction deliveryevaluation harnessesclassifier buildingdetector buildingscoring modelsexperiment designarchitecture iteration

Soft Skills

willingness to experimentstrong engineering instinctscomfort with ambiguitycollaborationproblem framingiterative improvementcommunicationownershiptrust in productioncritical thinking

Certifications

MSPhD