Senior ML Engineer (Technical Lead)

Zerolook is a company revolutionizing air travel pricing through machine learning. The Senior ML Engineer (Technical Lead) will play a pivotal role in shaping the technical approach to solve core problems in the industry. This position involves designing and implementing high-performance, low-latency ML models for fare and itinerary estimation, as well as building and maintaining robust data pipelines to process large volumes of historical fare and schedule data. The main responsibilities include developing confidence scoring systems for intelligent routing of traffic between ML models and legacy systems. The role also involves leading the team in hiring, mentoring, and developing core domain expertise in ML for fare and itinerary prediction. Additionally, the Senior ML Engineer will collaborate with founders and key industry organizations to develop Zerolook's core offerings. Candidates must have a Bachelor's or Master's degree in Computer Science or a related field, along with a broad knowledge of ML approaches and their strengths and weaknesses. At least four years of experience in designing, training, and deploying ML models in production environments is required. Strong software engineering skills and experience in building automated data pipelines are essential. Comfort with the startup environment, including ambiguity, fast iteration, and taking ownership, is also crucial. Experience with OTAs, metasearch, or GDS/NDC integration is a significant plus. Additionally, experience building large-scale applications serving over 10,000 queries per second with low latencies is beneficial. A Ph.D. in Computer Science or a concentration in Data Science is also a nice-to-have. What the role asks for: - Bachelor's or Master's degree in Computer Science or a related field. - Broad knowledge of ML approaches and their strengths and weaknesses. - 4+ years of experience in designing, training, and deploying ML models in production environments. - Strong software engineering skills with experience building robust, automated data pipelines. - Comfort with the "0 to 1" phase, ambiguity, fast iteration, and taking extreme ownership.