• Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks • Implement data pipelines for ingestion, preprocessing, feature engineering, and model training • Containerize ML models and applications using Docker for scalable and reproducible deployments • Deploy and maintain ML solutions in cloud environments (AWS/Snowflake) • Optimize model performance, latency, and resource utilization for real-time or batch inference • Monitor and troubleshoot ML models in production, ensuring reliability and robustness • Сollaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems • Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness • Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate

• Engineer systems that anticipate and neutralize security threats before they reach infrastructure. • Design, train, and deploy machine learning models directly at the edge. • Bridge the gap between security intelligence and massive-scale data science. • Collaborate with red teamers and security engineers to turn attack data into proactive defense mechanisms.

Localization Engineer Location: North Carolina, United States of America Full time Remote This is a fully remote role. Relocation assistance is NOT provided. What will you do in this role? Provides a full range of engineering and technical services on large and/or complex projects to analyze and prepare files to facilitate efficient and high-quality translations and implementation in the customer’s environment. Applies advanced technical expertise to ensure the highest quality and profitability on large and/or complex projects. Qualification requirements: Education requirements - Bachelor's degree is required, preferably in Translation Studies, Linguistics, Programming, Computer Science or similar fields. Experience requirements - Previous Localization Engineering experience is required. - Previous experience that provides the knowledge, skills, and abilities to perform the job (comparable to 5 years) - Experience working with various file formats, such as Json, Resx, Vtt, HTML, XML (all different types of XML from different systems), Madcap Flare, Dita, Articulate, mp4, Captivate, etc. is required. - Experience working with CAT/TMS tools, such as Wordbee, SDL Trados Studio, MemoQ, Memsource/Phrase, XTM, etc. is required. - Experience in troubleshooting localization and translation projects throughout the whole lifecycle is required. - Experience with Content Management Systems (Tridion, ST4) is preferred. Knowledge, Skills, Abilities - Performs and manages localization engineering tasks - Ability to convert the different formats to translatable form and then back-convert the translatable files into the original formats - Experience with parsers/scripting - Guides project team and/or external engineers on the various application conversion steps and raises questions/queries clearly - Helps resolve/fix tag issues in the translated files - Experience/knowledge in programming languages (Python, C++, C#) - Shares ideas for process automation or new tool suggestions - Updates/adds new configuration/settings in TMS/CAT Tool based on new process updates/requirements - Ability to work in a fast-paced environment - Ability to deliver under tight schedules - Strong communication and collaboration skills - Ability to manage multiple projects and meet deadlines - Knowledge of languages and cultural nuances - Certification in localization engineering or related fields (preferred) *Must be legally authorized to work in the US without sponsorship.

• You’ll take ownership of how product data is structured, validated, and used across the platform. • Improving how we classify and understand products at scale • Raising the overall quality of catalogue data and defining what “good” looks like • Ensuring product data supports effective search, filtering, and discovery • Identifying gaps in our catalogue and surfacing opportunities for growth • Improving how our catalogue performs across external channels • You’ll build and evolve the systems and decision logic that enable this, and iterate based on real-world performance and data. • You’ll work across: - Structured data (catalogue attributes, GTINs, taxonomy) - Unstructured data (text and images) - Behavioural data (search, clicks, conversions)