Machine Learning Engineer

Witness AI•Mountain View, CA

1d•$125,000 - $160,000•Hybrid

About The Position

WitnessAI is a fast-growing SaaS startup on a mission to enable enterprises to adopt AI, safely. We're building a product that provides security and governance guardrails for public and private LLMs. As a Machine Learning Engineer, you’ll design, build, and evaluate language models that power our AI security products. You’ll own the end-to-end pipeline — from dataset curation and preprocessing to experiment design, evaluation, and visualization of results. This role blends engineering and applied research, with an emphasis on producing reliable, interpretable, and safe language models.

Requirements

Experience: 2–5+ years working in machine learning or data science, ideally in a security or infrastructure-heavy environment.
Technical Skills: Strong software engineering background (Python, testing frameworks like pytest/unittest, CI/CD tools).
Proficiency in ML frameworks such as PyTorch.
Experience with data engineering tools (e.g., Spark, Kafka, Airflow).
Familiarity with deploying models on cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker, Kubernetes).
Strong knowledge of ML fundamentals (supervised/unsupervised learning, deep learning, NLP).
Security Awareness: Interest or background in cybersecurity, adversarial ML, anomaly detection, or related fields.
Startup Mindset: Comfortable working in fast-moving, ambiguous environments with a focus on shipping and iterating quickly.

Nice To Haves

Research or industry experience in adversarial ML, model robustness, or explainable AI.
Experience building interactive dashboards for model monitoring and visualization.
Contributions to open-source ML, NLP, or security projects.

Responsibilities

Build scalable pipelines to collect, preprocess, and manage datasets for training and evaluation of LLMs.
Design and run experiments to evaluate LLMs on accuracy, robustness, fairness, and safety.
Create dashboards, reports, and visualizations to communicate evaluation results, trends, and failure cases.
Develop and leverage knowledge graphs to structure data, enrich evaluation, and improve context-driven model performance.
Work with researchers to translate new ideas into engineering workflows, and with data scientists to automate QA checks and guardrails.
Fine-tune, optimize, and integrate models into production systems with a focus on reliability, scalability, and monitoring and CI/CD best practices.
Contribute to ML tooling and experimentation frameworks to accelerate iteration.