Senior Applied AI/ML Scientist - Search Ads at Faire

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

01 May, 26

Salary

264000.0

Posted On

31 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Natural Language Processing, Query Understanding, Real-Time Systems, Model Productionization, Data Analysis, Collaboration, Communication, Algorithm Development, Search Ranking, Recommendation Systems, Embedding-Based Retrieval, Product Development, Statistical Analysis, Performance Optimization

Industry

Software Development

Description

About Faire Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours. About this Role The Ads Data team is building the next generation of advertising products for the wholesale industry. As a key member of this team, you’ll help drive the ML algorithm strategy and system design behind one of the most critical levers for customer value and company growth—Search Ads. You’ll lead the advancement of real-time systems that decide which ads to show for a query, where to place them, and how to optimize for relevance, marketplace health, and advertiser outcomes. This role mirrors many of the technical expectations of Faire’s organic Search roles (modern NLP/LLMs, query understanding, real-time ranking), while operating in an ads environment with auctions, budgets, and pacing constraints. You’ll operate at the forefront of algorithms—combining large language models, natural language processing, query understanding, deep learning, and structured behavioral data to deliver highly relevant sponsored results for any given query. What You'll Do Own and evolve the Search Ads relevance stack—spanning query understanding, targeting, candidate generation, multi-stage ranking, and calibration—while meeting stringent latency and reliability goals. Design and productionize ML models that improve sponsored-result relevance and personalization, using a blend of unstructured signals (text/images where applicable), LLM-based representations, and structured marketplace features. Partner closely with engineering and product to connect relevance improvements to ads marketplace outcomes (e.g., conversion, long-term retailer experience, advertiser ROI), using rigorous offline evaluation and online experiments. As an early member of the Ads Data team, help define its roadmap and technical culture, leveraging deep product intuition to shape what ads at Faire should be—not just how they’re built. Work in a fast-paced, collaborative environment with team members who’ve shipped ML at top tech companies (e.g. Uber, Airbnb, Meta, Amazon, Pinterest). Qualifications 4+ years of experience building and shipping ML systems in production, with meaningful experience in search, recommendation, or ads ranking/retrieval. Hands-on experience with modern deep learning tooling (e.g., PyTorch) and familiarity with vector search / embedding-based retrieval concepts (e.g., Faiss, ScaNN, Pinecone). A strong track record of productionizing models that blend LLMs (e.g., BERT / GPT-class) with structured features to drive relevance and personalization. Product-focused mindset and a bias toward execution—moving quickly from research to measurable user and business impact. Strong communication skills and the ability to work with others in a closely collaborative team environment Great to Haves Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields Ability to quickly implement state of the art algorithms from an academic paper Salary Range San Francisco: the pay range for this role is $192,000 to $264,000 per year. This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future. Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting. Applications for this position will be accepted for a minimum of 30 days from the posting date. Why you’ll love working at Faire We are entrepreneurs: Faire is being built for entrepreneurs, by entrepreneurs. We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is taking part in the founding process. We are using technology and data to level the playing field: We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 small business owners. We build products our customers love: Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie - not steal a piece from it. Running a small business is hard work, but using Faire makes it easy. We are curious and resourceful: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality. Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog. Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form) Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

Responsibilities

You will own and evolve the Search Ads relevance stack, focusing on query understanding and multi-stage ranking. Additionally, you will design and productionize ML models to improve sponsored-result relevance and personalization.