• A Senior ML Engineer is responsible for designing, implementing, and maintaining AI systems across various applications. • They contribute to the organization's AI strategy, work on complex solutions and optimize existing systems to enhance performance. • Responsibilities include mentoring junior ML engineers and collaborating with cross-functional teams. • Develop AI applications and solutions by understanding business needs, collaborating with stakeholders, analyzing data, and implementing AI algorithms. • Design, develop, and maintain robust AI systems, including machine learning models and deep learning networks. • Document and demonstrate solutions with clear technical documentation, diagrams, and code comments. • Contribute to the organization’s AI strategy by researching cutting-edge tools and techniques, participating in educational opportunities, and maintaining professional networks. • Identify and resolve performance and scalability issues in AI applications by improving software and addressing bottlenecks and bugs. • Lead and collaborate with cross-functional teams to define and implement innovative AI solutions, optimizing user interaction and experience. • Conduct code reviews and mentor team members to uphold high coding standards. • Translate business requirements into actionable technical requirements. • Work closely with data engineering and data science teams to implement automated and unit testing. • Improve operations by analyzing systems and recommending procedural changes. • Support engineering goals by delivering project outcomes as needed.

• Design, build, and operate production-grade AI/ML systems that power Asteri’s orchestration platform • Collaborate closely with backend, platform, and frontend engineers to integrate AI capabilities into scalable, reliable product workflows • Deploy and iterate on LLM-based applications in production, continuously evaluating quality, latency, and cost • Own retrieval and agentic systems (e.g., RAG pipelines, workflow agents, policy-driven logic) end-to-end • Define and run rigorous evaluation and testing for AI systems, including offline experiments and production monitoring • Improve model performance and system behavior over time through experimentation, tuning, and system-level optimizations • Implement strong engineering practices for AI development, including testing, CI/CD, versioning, and rollback strategies • Stay current with advances in applied AI and generative models and translate relevant techniques into practical product improvements • Partner with cross-functional teams to understand product requirements and translate them into robust AI solutions

• Own end-to-end delivery of ML-based safety features, from technical design through production rollout and iteration • Build and maintain ML models that safeguard AI-generated content across multiple modalities (images, video, audio, text), detecting harmful content, IP violations, bias, and other safety concerns • Design and implement RAG (Retrieval-Augmented Generation) architectures and other advanced ML systems to enhance detection capabilities • Fine-tune and evaluate LLM-based models for content moderation and prompt filtering, making data-driven decisions about model selection and optimization • Collaborate with Legal, Product Policy, and AI product teams to define requirements, balance safety with user experience, and deliver compliant solutions • Create evaluation frameworks to measure model quality, safety coverage, false positive/negative rates, and policy alignment • Monitor production systems, respond to incidents, and maintain operational excellence through documentation and runbooks

• Propose and prototype innovative solutions to solve real-world problems, leveraging the latest state-of-the-art techniques in the field • Develop and maintain core ML pipelines • Train and deploy deep learning models for real-time applications • Collaborate cross-functionally with camera, systems and labeling teams • Curate datasets for evaluating performance and comparing performance trends over time • Provide technical mentorship to other junior ML engineers