{"id":4343022,"url":"https://alion.io/job/true-talent-solutions-partners-founding-engineer-57","title":"Founding Engineer","company":{"id":4415660,"name":"True Talent Solutions Partners","domain":"truetalentsolutionspartners.com","url":"https://alion.io/company/true-talent-solutions-partners","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reranking","optional":false},{"name":"GitHub","optional":true}],"status":"live","first_seen_at":"2026-11-15T17:58:30Z","employer_posted_date":"2026-11-15","last_verified_at":"2026-11-15T19:50:39Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nJoin a small engineering team at an early-stage ad technology company to build and evolve the systems that decide which ads reach users in real time. You will own work across data pipelines, machine learning, and production serving, with direct impact on the recommendation system and its business outcomes.\nWhat You'll Do\nDesign and ship low-latency ad retrieval, ranking, and reranking systems that balance advertiser performance with user experience.\n\nBuild data pipelines and feature infrastructure to train models using reward signals that arrive on different timelines, from clicks to conversions.\n\nDevelop user and context representations from conversational data, engagement history, and signals such as device, location, and session context.\n\nBuild serving infrastructure designed for sub-second response times and cost efficiency.\n\nCreate generative agents that produce playable and video ad variations, and feed their performance back into the recommendation system.\n\nWhat We're Looking For\nExperience level: 0 to 3 years, with at least one ML or AI system shipped to production.\n\nEnd-to-end experience with backend systems, data pipelines, model training, and production serving, ideally including a recommendation or ranking system.\n\nStrong systems fundamentals and the ability to reason from first principles across infrastructure for large user volumes, classical machine learning, and AI agents.\n\nExperience with PyTorch is relevant; AdTech or recommendation systems experience is a plus.\n\nAn active GitHub or shipped side projects demonstrating work across backend, ML, or infrastructure.\n\nInterest in games and playable ad formats is valued. Startup experience, mentoring, or experience building AI-native products is also relevant.\n\nMust be a US citizen or green card holder and able to work on-site in San Francisco five days per week.\n\nCompensation & Benefits\nBase salary is $200,000 to $250,000 annually, with equity. 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