{"id":327657,"url":"https://alion.io/job/witnessai-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":63230,"name":"WitnessAI","domain":"witness.ai","url":"https://alion.io/company/witnessai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":36000,"max":60000,"currency":"USD","period":"year","gross":null,"usd_annual":60000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false},{"name":"Explainable AI","optional":true}],"status":"live","first_seen_at":"2026-09-04T15:44:34Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-11-01T18:48:56Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"Job Title: Machine Learning Engineer\nLocation: Cairo\nType: Full-time\nTeam: Machine Learning\nAbout Us\nWitness 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.\nThe Role\nAs 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.\nWhat You’ll Do\nBuild scalable pipelines to collect, preprocess, and manage datasets for training and evaluation of LLMs.\n\nDesign and run experiments to evaluate LLMs on accuracy, robustness, fairness, and safety.\n\nCreate dashboards, reports, and visualizations to communicate evaluation results, trends, and failure cases.\n\nDevelop and leverage knowledge graphs to structure data, enrich evaluation, and improve context-driven model performance.\n\nWork with researchers to translate new ideas into engineering workflows, and with data scientists to automate QA checks and guardrails.\n\nFine-tune, optimize, and integrate models into production systems with a focus on reliability, scalability, and monitoring and CI/CD best practices.\n\nContribute to ML tooling and experimentation frameworks to accelerate iteration.\n\nWhat We’re Looking For\nExperience: 2-5+ years working in machine learning or data science, ideally in a security or infrastructure-heavy environment.\n\nTechnical Skills:\nStrong software engineering background (Python, testing frameworks like pytest/unittest, CI/CD tools).\n\nProficiency in ML frameworks such as PyTorch.\n\nExperience with data engineering tools (e.g., Spark, Kafka, Airflow).\n\nFamiliarity with deploying models on cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker, Kubernetes).\n\nStrong knowledge of ML fundamentals (supervised/unsupervised learning, deep learning, NLP).\n\nSecurity Awareness: Interest or background in cybersecurity, adversarial ML, anomaly detection, or related fields.\n\nStartup Mindset: Comfortable working in fast-moving, ambiguous environments with a focus on shipping and iterating quickly.\n\nNice to Have\nResearch or industry experience in adversarial ML, model robustness, or explainable AI.\n\nExperience building interactive dashboards for model 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