Role Description Brave is looking for an experienced Quality Assurance Engineer to focus on the quality of Brave's browser releases. You'll work with various groups across Brave, cutting across both functionality and departments. This is a high-profile and impactful, hands-on position that will require grit. Work together with our developers and privacy/security experts to help us take our browser to the next level. Qualifications - 5+ years experience with testing software - Strong written and verbal communication skills in English - Experience in macOS, Windows, and various Linux distributions - Android and iOS experience - Experience with both Android Studio and Xcode - Experience using packet monitoring tools (Charles Proxy, Fiddler, Wireshark) - Familiarity with cryptocurrency wallets and ecosystem - Familiarity with latest AI trends and ecosystem - Experience with git and other command line tools - Comfortable working in an open source setting and with GitHub - A passion for helping protect users’ privacy and security - Proven record of getting things done Requirements - 5+ years experience with testing software - Strong written and verbal communication skills in English - Experience in macOS, Windows, and various Linux distributions - Android and iOS experience - Experience with both Android Studio and Xcode - Experience using packet monitoring tools (Charles Proxy, Fiddler, Wireshark) - Familiarity with cryptocurrency wallets and ecosystem - Familiarity with latest AI trends and ecosystem - Experience with git and other command line tools - Comfortable working in an open source setting and with GitHub - A passion for helping protect users’ privacy and security - Proven record of getting things done Benefits - Industry-leader in privacy, with a research and engineering team that’s innovating every day to keep people safer online and beat Big Tech - Highly competitive salaries & benefits, and generous home-office stipends - Fully remote team (no office, no commute) - Welcoming, humble, ridiculously smart teammates, and a truly flat org structure - Opportunity to get in early at a hyper-growth company, and revolutionize the web - Salary range for this role is $90,000 - $130,000 USD, depending on experience

• Develop and Implement: Create threat detection rules, alerts, and dashboards using Splunk, SentinelOne, and other security tools. • Should be comfortable creating SIGMA rules in YAML. • Analyze Logs: Examine security logs and alerts to identify and investigate potential security incidents. • Continuously monitor security logs and network traffic for threats, breaches, and unusual activity. • Embed that activity into detection logic and security controls. • Collaborate: Work closely within the security team to enhance our overall security posture. • Administer applications within the EVERSANA security stack. • Stay Informed: Keep up-to-date with the latest threats, vulnerabilities, and security technologies. • Maintain Documentation: Contribute to the development and upkeep of security detection database. • Will be responsible for maintaining a list of currently deployed detection rules. • Automate Processes: Streamline security tasks and processes to boost efficiency and effectiveness. • Incident Response : Respond to security incidents, troubleshoot issues, and remediate as required. • Demonstrate a commitment to diversity, equity, and inclusion through continuous development, modeling inclusive behaviors, and proactively managing bias.

Role Description The Sr. Machine Learning Engineer is responsible for designing, building, and deploying machine learning systems that power AI-driven features across Mirion's products. This role combines hands-on modeling and ML infrastructure work with technical leadership — driving best practices for the ML lifecycle, mentoring engineers, and partnering with stakeholders to translate business problems into production-grade ML solutions. - Design, train, and deploy machine learning models for applied use cases across radiation safety, nuclear energy, and nuclear medicine. - Architect end-to-end ML systems, including training pipelines, model serving infrastructure, and monitoring. - Lead technical design reviews and mentor junior ML engineers on modeling, MLOps, and architectural best practices. - Establish standards for model evaluation, experiment tracking, reproducibility, and responsible AI across the team. - Partner with the Data Platform team to define feature requirements and ensure ML workloads are well-supported by the underlying data infrastructure. - Collaborate with stakeholders and product partners to translate business problems into well-scoped ML solutions. - Drive optimization initiatives for model performance, inference cost, and reliability in production. - Participate in hiring and team building for the Applied AI function. - Contribute to architectural decisions and long-term ML strategy. - Troubleshoot production model issues — drift, degradation, and pipeline failures — and implement robust monitoring and alerting. Qualifications - 5+ years experience in machine learning engineering, applied ML, or related field. - Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or similar). - Deep experience taking ML models from research/prototype through to production deployment. - Hands-on experience with ML infrastructure — training pipelines, model serving, experiment tracking, and monitoring. - Solid software engineering fundamentals: testing, code review, version control, and CI/CD. - Working knowledge of SQL and modern data warehouses or lakehouses (Snowflake, BigQuery, Databricks, etc.). - Experience with cloud platforms (AWS, GCP, or Azure) at scale. - Proven ability to mentor and guide junior engineers. Preferred Qualifications - Experience building applied AI products or ML platforms from the ground up. - Experience with Databricks, MLflow, and lakehouse-based ML workflows. - Expertise with LLMs, RAG systems, or generative AI applications in production. - Experience with feature stores, vector databases, and real-time inference architectures. - Knowledge of model governance, model lineage, and responsible AI practices. - Background in regulatory-heavy industries or complex compliance requirements. - Experience with infrastructure-as-code and MLOps practices. - Background in computer vision, time-series, or signal processing (relevant to radiation detection data).

• Build Highly Available Media Infrastructure: Design and implement fault-tolerant media services that power real-time video communication. When a Deaf user needs an interpreter, your pipelines connect them instantly and reliably. • Own the Real-Time Media Stack: Take architectural responsibility for our WebRTC and GStreamer-based media pipelines, specifying how streams are negotiated, composed, routed, and recovered. You'll make the structural decisions (SFU strategy, pipeline topology, failure modes, integration with the rest of our backend) that ensure calls connect fast and stay up. • Tune for Quality: Diagnose and improve perceived media quality through measurement and iteration—encoder tuning, congestion control and bandwidth estimation, loss recovery (NACK, FEC), and jitter buffer behavior. You'll instrument the pipeline, find where quality degrades, and adjust. • Enable Product Teams Through Platform Excellence: Build the APIs and services that frontend and backend engineers depend on to deliver great user experiences. You'll design intuitive contracts, provide clear documentation, and ensure your media platform is a force multiplier for the team. • Deliver Operational Excellence and Observability: Instrument what matters—getStats() metrics, MOS-style quality scores, pipeline health—establish SLOs that reflect user impact, and build systems that surface problems before users notice. You own uptime: incidents, post-mortems, and continuous improvement. • Contribute to a Purpose-Driven, User-Centered Culture: Understand how platform decisions impact real users, advocate for reliability in technical discussions, and connect your work to our mission of improving access and communication for the Deaf community.