Role Overview
This is an individual contributor role blending ML research, applied ML, and software engineering. Topics include trust and safety challenges for large language models (LLMs) and multimodal LLMs (MM-LLMs):
- Prompt injection attacks and adversarial robustness
- Safety alignment and guardrails
- Privacy and confidentiality
- (Mechanistic) interpretability
Responsibilities
- Designing and executing research experiments on LLM trust and safety
- Reading, synthesizing, and producing research papers and technical write-ups
- Building, running, and owning systems end-to-end
- Contributing to core product capabilities as a founding team member
What We're Looking For
ML & Research
- Demonstrated research experience; PhD in a technical field strongly preferred
- Ability to identify relevant research questions and find answers through literature review or experimentation
- First-author or major contributing authorship on peer-reviewed publications; please list these on your resume or cover letter
- Hands-on experience with PyTorch, HuggingFace (Transformers, Datasets), and applied deep learning
- Experience training and evaluating deep learning models
- Nice to have: LLM fine-tuning, multimodal LLMs, PEFT/LoRA
- Nice to have: Familiarity with ML interpretability methods (mechanistic interpretability, sparse autoencoders, linear probes, NLP/Vision interpretability)
Software Engineering
- Proficiency in Python; experience with Jupyter notebooks
- Unix environments, Git, and basic AWS/GCP usage
- Nice to have: Programming languages well-roundedness; experience with statically-typed and functional programming languages
- Nice to have: Familiarity with (LLM) deployment tooling like Docker, vLLM, Triton Inference Server or similar
Logistics
- Available to start within 2 months
- Role may include limited on-call responsibilities tied to production ownership
Additional Information
- This is a founding, high-ownership role with direct impact on core product capabilities.
- You will be expected to build, run, and own systems end-to-end.
- The role may include limited on-call responsibilities aligned with production ownership.
About RealmLabs
- AI systems misbehave: they leak sensitive data, get manipulated through prompt injections, or behave in ways their builders never intended. RealmLabs is building the infrastructure to detect, debug, and prevent these behaviors.
- Our approach is to secure AI from within: observing models from the inside, extracting signals relevant to their behavior and misbehavior, and patching them on the inside, rather than relying on input/output filters or external guardrails.
- We are a pre-seed startup and a finalist in the RSAC 2026 Innovation Sandbox. Our customers include Anthropic, a leading ride-sharing platform, and a Big 3 management consulting firm.
- This is a founding engineer role with meaningful equity at an early-stage company.
Compensation & Benefits
- Market aligned compensation and benefits.
- Founding equity (Equity is a significant component of this role and will be discussed)
- Medical, Dental, Vision, Life insurance, 401-K, In-office lunch etc.
Visa sponsorship
- We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and candidate. But if we make you an offer, we will make all reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
Compensation
The base pay range for this role is $200,000 – $260,000 per year.
