Senior Machine Learning Engineer, Cybersecurity / Threat Detection at Keeper Security, Inc.

Remote, USFull-timeRemoteDevelopment (other)Posted 4 months ago

About the Role

Description We are seeking a highly motivated and experienced Machine Learning Engineer to join our AI & Threat Analytics team. This is a 100% remote position with an opportunity to work a hybrid schedule for candidates based in the El Dorado Hills, CA or Chicago, IL metro area! Keeper’s cybersecurity software is trusted by millions of people and thousands of organizations globally. Keeper is published in 23 languages and sold in over 150 countries. Join one of the fastest-growing cybersecurity companies and play a critical part in advancing Keeper’s AI-driven threat detection capabilities for our Privileged Access Management (PAM) platform. About Keeper Keeper Security is transforming cybersecurity for people and organizations around the world. Keeper’s affordable and easy-to-use solutions are built on a foundation of zero-trust and zero-knowledge security to protect every user on every device. Our award-winning, zero-trust, privileged access management platform deploys in minutes and seamlessly integrates with any tech stack and identity application to provide visibility, security, control, reporting and compliance across an entire enterprise. Trusted by millions of individuals and thousands of organizations, Keeper is an innovator of best-in-class password management, secrets management, privileged access, secure remote access and encrypted messaging. Learn more at <a href="https://www.keepersecurity.com/">KeeperSecurity.com</a>. About the Role You will tackle one of the most critical challenges in cybersecurity: detecting threats within privileged access sessions with high accuracy and low latency. Privileged accounts are prime targets for attackers, and the ML systems you build will serve as a first line of defense against anomalous and malicious behavior across SSH, RDP, VNC, and database connections. This role focuses on a hybrid detection approach combining vision-language models (VLMs) and domain-adapted ML models. You will work in a Python-based environment processing real-time session data via WebSocket, WebRTC, and protocol-level interfaces. The role is well-suited for engineers who enjoy both research-oriented work (datasets, evaluation, model training) and applied production engineering (inference systems, integration, and optimization). Responsibilities <ul> <li>Design, curate, and maintain datasets for training and evaluating threat detection models</li> <li>Build custom ML models for domain-specific threat classification and risk assessment</li> <li>Engineer and optimize prompts for vision-language models to analyze session behavior</li> <li>Create evaluation frameworks and benchmarks to measure accuracy, robustness, and reliability</li> <li>Develop Python-based inference services within Dockerized environments</li> <li>Integrate AI/ML capabilities with WebSocket, WebRTC, and low-level system interfaces for real-time analysis</li> <li>Write clean, maintainable code and produce clear technical documentation</li> <li>Monitor, troubleshoot, and optimize models in production for performance, scalability, and reliability</li> </ul> Requirements <ul> <li>5+ years of professional experience in machine learning research or development</li> <li>Strong proficiency in Python</li> <li>Hands-on experience with dataset collection, curation, and labeling for ML training</li> <li>Experience designing model evaluation frameworks and performance benchmarks</li> <li>Experience working with vision-language models or large language models (e.g., GPT, Claude, Gemini, Qwen)</li> <li>Familiarity with prompt engineering techniques and LLM frameworks</li> <li>Experience building and deploying ML inference systems using Docker</li> <li>Working knowledge of graph data structures and their practical applications</li> <li>Familiarity with Git-based workflows and model repositories (e.g., Hugging Face)</li> <li>Experience using cloud platforms for ML deployment and inference (AWS, GCP, and/or Azure)</li> <li>Bachelor’s or Master’s degree in Computer Science, Machine Learning, Cybersecurity, or equivalent practical experience</li> <li>U.S. Person status required due to GovCloud involvement</li> </ul> Preferred Qualifications <ul> <li>Experience with security, fraud, abuse detection, or anomaly detection systems</li> <li>Familiarity with PAM, identity, or privileged access environments</li> <li>Exposure to AWS Bedrock or similar managed AI services</li> <li>Knowledge of network protocols and low-level system interfaces</li> </ul> Benefits <ul> <li>Medical, Dental & Vision (Inclusive of domestic partnerships)</li> <li>Employer Paid Life Insurance & Employee/Spouse/Child Supplemental life</li> <li>Voluntary Short/Long Term Disability Insurance</li> <li>401k (Roth/Traditional)</li> <li>A generous PTO plan that celebrates your commitment and seniority (including paid Bereavement/Jury Duty, etc)</li> <li>Above market annual bonuses</li> </ul> Keeper Security, Inc. is an equal opportunity employer and participant in the U.S. Federal  E-Verify program. We celebrate diversity and are committed to creating an inclusive environment for all employees. Classification: Exempt<div class="content-conclusion">  Keeper Candidate Privacy Notice This notice explains how Keeper Security processes your personal data during recruitment. Depending on the role and location, the Controller of personal data (the organization responsible for determining why and how personal data is processed) will be Keeper Security Inc. (US), Keeper Security EMEA Ltd. (Ireland), or Keeper Security APAC K.K (Japan). 1. Data We Collect Information You provide: <ul> <li class="pf1">Contact details, CV/resume, cover letter</li> <li class="pf1">Employment history, qualifications, work eligibility</li> <li class="pf1">Application responses and uploaded documents</li> </ul> Information We generate: <ul> <li class="pf1">Interview notes, assessments, communications</li> <li class="pf1">Scheduling information</li> </ul> Information From Others: <ul> <li class="pf1">Recruiter/referral information who submit your profile</li> <li class="pf1">References (with your consent, before final offer)</li> <li class="pf1">Public professional profiles</li> <li class="pf1">Background verification (post offer)</li> </ul> Voluntary Diversity and Equal Opportunity Information <ul> <li class="pf1">We may ask you to voluntarily provide diversity information including race/ethnicity, gender, disability status and veteran status (US). Providing this information is optional and Keeper collects this data in order to comply with EEOC and similar requirements</li> </ul> 2. How We Use Your Data <ul> <li class="pf1">Assess your application and suitability</li> <li class="pf1">Manage interviews and recruitment workflow</li> <li class="pf1">Consider you for other/future roles (we may seek your consent to keep your information on our systems beyond the retention period specified)</li> <li class="pf1">Comply with employment law obligations</li> </ul> 3. Legal Basis <ul> <li class="pf1">Legitimate Interests (recruitment management, security and integrity of the hiring process)</li> <li class="pf1">Contracting steps (for progressed candidates)</li> <li class="pf1">Legal and regulatory compliance obligations; explicit consent where required</li> </ul> 4. Who We Share Information With Internal: <ul> <li class="pf1">HR, hiring managers, interviewers*, IT support for system administration</li> </ul> *Note - diversity and equal opportunity data is not shared with hiring managers. Third Parties: Service providers who assist with: <ul> <li class="pf1">Applicant tracking, recruitment systems and assessment providers</li> <li class="pf1">Background verification vendors (post offer)</li> <li class="pf1">Recruitment agencies (where applicable)</li> <li class="pf1">Tools to support communication, collaboration and to securely store your data</li> </ul> Keeper ensures that all our third parties are contractually bound to protect your personal data with adequate safeguards in place. 5. International Transfers Your data may be accessed by Keeper entities globally as needed for the purposes of hiring and decision making. We protect any such data transfer between Keeper entities using appropriate safeguards under applicable data protection laws. 6. Security We implement appropriate technical and organizational measures to protect your data, consistent with our industry leading security standards. 7. Retention We keep your data for 24 months from your last application activity, then delete or anonymize it. Exceptions: <ul> <li class="pf1">You opt into our talent database for further retention by providing consent (extended retention)</li> <li class="pf1">You're hired (transfers to employee records)</li> </ul> 8. Your Rights You have the following rights and can contact us at the email below to exercise them: <ul> <li class="pf1">Access, correct, or delete your data, subject to applicable law and retention requirements</li> <li class="pf1">Object to or restrict processing</li> <li class="pf1">Withdraw consent (where applicable)</li> <li class="pf1">Request data portability</li> <li class="pf1">Lodge a complaint with your data protection authority</li> </ul> If you become an employee, your rights regarding your employee record are governed by our internal Employee Privacy Notice and certain data will be retained as required under relevant laws such as employment or tax law.  When you request access to your personal data, some information may be redacted if it includes the personal data of other individuals or information that we must protect in order to preserve their privacy rights. 9. Automated Decisions Keeper does not make hiring decisions using solely automated processing. 10. Contact - Candidates can send privacy questions to: [email protected]  </div>

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