Machine Learning Engineer

Machine Learning About Us Witness AI invented intent-based AI security. While legacy tools monitor what users say to AI, we understand what they're trying to accomplish - stopping jailbreaks, data exfiltration, and shadow AI before damage occurs. We provide visibility into how employees and systems use AI - capturing prompts, responses, and agent activity - so security teams can monitor risk, investigate incidents, and enforce guardrails in real time.

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. What You’ll Do 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. What We’re Looking For 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.

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. Salary Range $36,000-$60,000 (The exact salary will be determined based on the selected candidate’s location, qualifications, experience, and relevant skills.)