Senior ML Engineer (Technical Lead)

Zerolook is seeking a Senior ML Engineer (Technical Lead) to leverage machine learning (ML) skills in transforming how air travel pricing is handled. The company focuses on innovating air travel pricing through ML, and the role involves defining the technical approach for this core aspect of their operations. The main responsibilities include designing and implementing high-performance, low-latency ML models for fare and itinerary estimation. This involves building and maintaining robust data pipelines to process large volumes of historical fare and schedule data. Additionally, the role includes developing confidence scoring systems for intelligent routing of traffic between ML models and legacy systems. The position also requires developing core domain expertise in using ML for fare and itinerary prediction, as well as leading and mentoring other engineers as the team expands. Strategic collaboration with industry leaders and founders is also a key part of the role. To qualify, candidates must have a Bachelor's or Master's degree in Computer Science or a related field. They should have broad knowledge of ML approaches and their strengths and weaknesses, as well as 4+ years of experience in designing, training, and deploying ML models in production environments. Strong software engineering skills and experience in building robust, automated data pipelines are essential. Comfort with the startup environment, including ambiguity, fast iteration, and taking extreme ownership, is also required. Experience with Online Travel Agencies (OTAs), metasearch, or GDS/NDC integration is a significant plus. Additionally, experience building large-scale applications serving over 10,000 queries per second (QPS) with latencies of around 1 second is beneficial. A Ph.D. in Computer Science and/or a concentration in Data Science is also a bonus. What the role asks for: - Bachelor's or Master's in Computer Science or related field. - 4+ years of experience in designing, training, and deploying ML models in production. - Strong software engineering skills with experience in building robust, automated data pipelines. - Comfort with the "0 to 1" phase, ambiguity, fast iteration, and taking extreme ownership. - Familiarity with the breadth of ML approaches and their relative strengths and weaknesses. - Previous experience with OTAs, metasearch, or GDS/NDC integration (nice-to-have). - Experience building applications serving over 10,000 QPS at latencies of around 1 second (nice-to-have). - Ph.D. in Computer Science and/or educational concentration in Data Science (nice-to-have).