• We are seeking a highly skilled and innovative Machine Learning Engineer with 4-6 years of experience, specializing in Generative AI and Agentic AI, to join our dynamic team. • This role is critical for designing, developing, deploying, and maintaining advanced AI solutions that leverage large language models (LLMs) to create intelligent, autonomous agents capable of performing complex tasks and driving significant business value within the telecom sector. • The ideal candidate will possess extensive hands-on experience across the entire Generative AI lifecycle, with a strong focus on MLOps, performance monitoring, and ensuring the robustness and scalability of AI systems in production. • Collaborate closely with data scientists, product managers, and other engineering teams to define requirements, design solutions, and deliver high-impact AI products. • Develop strategies for continuous improvement, model retraining, and A/B testing in production environments.

• Apply data analysis techniques to solve business problems within the defined scope • Conduct exploratory data analysis (EDA) to identify patterns, trends, and opportunities • Prepare data and perform feature engineering for model building • Develop, test, and validate Machine Learning models • Evaluate model performance using appropriate metrics • Support the interpretation of results and the generation of actionable insights • Communicate analytical results to technical and business stakeholders • Collaborate with cross-functional teams (business, engineering, IT) • Validate hypotheses, assumptions, and model outputs with the relevant areas

Role Description Fetch is looking for a Senior Director, Machine Learning to lead the strategy and execution behind how we optimize and deliver offers and ads across the Fetch platform. This leader will own a critical business and technical domain spanning: - Ads delivery - Ads infrastructure - Ranking - Relevance - Forecasting - Pacing - Personalization - Optimization You will lead managers, technical leads, and senior ICs responsible for high-scale ML systems that power user engagement, advertiser outcomes, and business growth. This role requires a leader who has operated at meaningful scale, brings deep technical judgment from prior hands-on experience, and can set direction across complex systems where user experience, partner performance, platform quality, and monetization must be balanced. The ideal candidate understands the end-to-end ads, recommendations, or personalization funnel and has strong opinions on: - Relevance - Ranking quality - Experimentation - Optimization - Marketplace trade-offs You will influence technical strategy across teams, coach leaders, and hold a high bar for delivery, technical rigor, and organizational health. Qualifications - 10+ years of experience in machine learning, software engineering, data science, or a related technical discipline. - 5+ years of experience leading teams, managers, or technical leaders in a high-scale technology environment. - Experience operating ML, ranking, ads, recommendations, personalization, or optimization systems at scale. - Deep experience with ads delivery, ranking, relevance, personalization, forecasting, pacing, marketplace quality, or optimization. - Strong technical foundation developed through prior hands-on experience as an engineer, ML practitioner, applied scientist, data scientist, or technical lead. - Proven ability to define technical direction across multiple teams, including system architecture, platform capability, and engineering leverage. - Strong judgment around ML system design, experimentation, model evaluation, ranking quality, marketplace dynamics, and technical trade-offs. - Experience influencing senior stakeholders and aligning cross-functional partners around technical priorities and business outcomes. - Experience coaching managers, technical leads, and senior ICs. - Track record of building inclusive, high-performing teams with strong ownership, engagement, and execution discipline. Requirements - Experience leading ads delivery, ad ranking, ad relevance, ads infrastructure, or ads marketplace teams at a scaled consumer technology, retail media, marketplace, social, search, gaming, or ad tech company. - Experience with large-scale recommendations, personalization, retrieval and ranking systems, auction dynamics, yield optimization, or real-time decisioning platforms. - Experience with forecasting, pacing, budget optimization, campaign delivery, or performance prediction. - Experience working in a two-sided or multi-sided marketplace. - Familiarity with experimentation platforms, A/B testing, offline and online evaluation, causal measurement, model monitoring, and production ML operations. - Experience with modern ML tooling, feature platforms, real-time serving systems, distributed data processing, and cloud-based infrastructure. - Experience partnering with go-to-market or commercial teams on advertiser, brand, or partner-facing outcomes. - Experience scaling systems, teams, and processes in a high-growth environment. Benefits At Fetch, we offer competitive compensation packages including base salary, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at: https://fetch.com/careers

• Next-Gen budget pacing: Design and optimize sophisticated pacing controllers (PID, probabilistic forecasting) to smooth advertiser spend across diverse time zones and traffic spikes, ensuring optimal delivery and marketplace stability. • Creative GenAI infrastructure: Lead the backend integration of Generative AI models (Diffusion, LLMs) to automate the creation of high-performing image and video assets tailored to specific campaign goals and formats. • Marketplace experimentation engine: Build and scale the infrastructure for high-velocity experimentation, including A/B testing, switchback tests, and long-term holdouts to measure the impact of marketplace changes on advertiser ROI and platform health. • High-Scale billing reliability: Architect and maintain high-throughput billing pipelines that process billions of events with 100% accuracy, bridging the gap between real-time ad delivery and mission-critical financial reconciliation. • Scientific optimization: Analyze complex financial and marketplace datasets to refine the trade-off between spend velocity and advertiser performance, using experimentation results to tune pacing and billing logic.