Develop advanced machine learning solutions to extract meaning, context, and structure from audio, text, and multimodal content for content enrichment and personalization.
Responsibilities
Design, develop, deploy, and improve machine learning models for content understanding
Apply deep learning, transformer architectures, large language models, and multimodal learning approaches
Own complex machine learning projects from problem definition through deployment and optimization
Evaluate model performance using offline metrics and experimentation frameworks
Collaborate with Product, Engineering, Analytics, and Data teams
Lead the design, development, and evolution of advanced machine learning systems supporting search relevance, retrieval, ranking, query understanding, and personalization across diverse audio content types.
Responsibilities
Lead the design, development, and evolution of advanced machine learning systems supporting search relevance, retrieval, ranking, query understanding, and personalization across diverse audio content types.
Define technical approaches and drive execution for highly complex search and discovery initiatives that span multiple products, systems, and stakeholder groups.
Develop and improve transformer-based, deep learning, semantic retrieval, and generative AI solutions that enhance content discovery and user engagement.
Establish and refine methodologies for search evaluation, experimentation, offline relevance measurement, and online testing to ensure continuous improvement of user experiences.
Partner closely with Product, Engineering, Data Science, Analytics, and Content teams to influence roadmap decisions and align technical investments with business priorities.
Requirements
M.S. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, or a related quantitative field
Lead the architecture, design, and implementation of high-performance backend systems for Akamai’s API Team.
Compensation
For US based candidates only - the base salary for this position ranges from $146,400 - $263,600/year; a candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, certifications and location. Compensation for candidates outside the US will vary. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP).
Responsibilities
Architect high-performance REST APIs and service layers using Python and Flask
Design robust schemas and optimize database queries using SQLAlchemy
Lead technical design reviews and mentor junior and mid-level engineers
Implement authentication, rate limiting, and observability across distributed backend services
Design fault-tolerant backend services with circuit breakers and defensive validation
Lead the architecture, development, deployment, and ongoing evolution of AI initiatives across a global consumer SuperApp. This is a hands-on technical leadership role focused on open-source foundation models, self-hosted infrastructure, and production AI systems.
Compensation
Compensation is accrued and payable following funding, after which it transitions to a regular salary, equity ownership, and OleX token participation in the overall OleOle group of companies.
Responsibilities
Establish overall AI architecture
Lead initiatives including AskOle
Lead real-time multilingual translation
Develop AI-powered sports history and content generation systems
Develop the sports data engine
Requirements
Nice to have: building consumer AI platforms at scale
Nice to have: multilingual and multimodal AI engineering
Lead end-to-end development of GenAI applications, including prompt engineering, fine-tuning, RAG pipelines, and LLM-based workflows, while ensuring high standards of performance, scalability, and responsible AI practices.
Responsibilities
Implement Agentic AI systems leveraging frameworks such as LangChain, Google ADK, Strands, and Semantic Kernel
Apply advanced prompt engineering techniques to improve accuracy and robustness of LLM systems
Build multimodal AI solutions integrating text with images or structured data
Apply harness engineering practices to design, test, and evaluate AI/agent workflows
Develop reusable agent tooling and integrations for enterprise services
Requirements
Master's degree in a relevant quantitative, applied field (Statistics, Econometrics, Computer Science, Mathematics, Engineering)
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