{"id":1262053,"url":"https://alion.io/job/wildcard-founding-applied-ml-engineer","title":"Founding Applied ML Engineer","company":{"id":3176126,"name":"Wildcard","domain":"wild-card.ai","url":"https://alion.io/company/wild-card","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Work at a Startup","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":130000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"FastAI","optional":false},{"name":"Scale AI","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Function Calling","optional":true},{"name":"LLM","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"SQL","optional":true},{"name":"Tool Use","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-09-25T20:43:38Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-29T17:59:38Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Founding Applied ML Engineer\nAbout Wildcard\nWildcard is the agentic commerce optimization platform for ecommerce and retail brands.\nWe help brands understand, improve, and monetize how their products show up across AI shopping agents. We’re building the mission control for agentic commerce: visibility (AEO & GEO), recommendations, execution, attribution, and automation in one platform.\nAs shopping shifts from traditional search to AI agents, brands need to know where they appear, why competitors are winning, what to change, and whether those changes drive real business outcomes.\nWe’re growing 50% month over month.\nWho you’ll work with\nYou’ll work directly with me, Kaushik Mahorker, founder of Wildcard.\nPreviously at Scale AI, I built the ecommerce enrichment engine behind the company’s largest pilot across 400K SKUs, 2.8M attributes, and hundreds of taxonomies, helping secure $15M+ in contracts with major retailers and marketplaces.\nThat experience made something clear: shopping discovery is being rebuilt for an AI-first world, and most brands are not prepared for the shift.\nThe role\nWe’re looking for a Founding Applied ML Engineer to help shape both the product and the company from the earliest stage.\nThis is engineer number one. You are not joining an engineering team. You are helping build one.\nThe ideal person is strong enough to own product engineering across the stack, but also has the applied ML judgment to build reliable AI systems, ranking systems, evals, attribution models, agents, and automation loops that customers can actually trust.\nThis is not a pure research role. It is not a pure analytics role. It is not a narrow full-stack role either.\nWe need a builder who can move between product, infrastructure, applied ML, data, and customer problems without waiting for someone else to define the lane.\nYou’ll work directly with customers, own product and infrastructure, and help decide what gets built, how it gets built, and what we prioritize as the market evolves.\nWe are looking for someone high-agency, fast-moving, and expert-level with AI coding tools. You should use AI to move significantly faster, but not outsource your judgment to it.\nThis market is moving fast. AI shopping agents, agentic commerce protocols, and consumer behavior are all changing in real time. The ambiguity is the opportunity.\nWeek 0 projects\nYou may work on:\nBuilding custom ML models to classify prompts, predict opportunity, and prioritize what brands should optimize for\nBuilding incrementality and attribution systems that connect AI visibility to revenue outcomes for ecommerce brands\nBuilding prompt discovery systems that identify and predict what shoppers are asking across AI commerce surfaces\nDesigning ranking, scoring, and evaluation systems for noisy AI commerce outputs\nModeling site traffic, conversion patterns, and performance trends from messy real-world data\nMaking core AI workflows reliable with queues, retries, observability, evals, and workflow orchestration\nBuilding agents that can recommend, execute, and validate changes across ecommerce sites\nDesigning pipelines to collect new signals and turn them into usable product intelligence\nAdapting the product to emerging agentic commerce protocols and platform launches\nMigrating scrappy early systems into scalable product infrastructure without slowing down execution\nWe’re looking for someone who\nHas prior founding experience, or was early at a Seed, Series A, Series B, or similarly fast-moving company\nHas strong full-stack experience and can ship independently across the stack\nHas applied ML or data science experience, especially with LLMs, ranking, retrieval, evals, attribution, experimentation, or product intelligence\nCan move between modeling, analysis, implementation, and product decisions\nIs high-agency, self-directed, and able to turn ambiguity into shipped product\nIs expert-level with AI coding tools and uses them to move significantly faster\nHas strong judgment on when to use AI and when not to\nCan reason about model behavior, failure modes, and quality without needing perfect data\nMoves fast, focuses on outcomes, and knows how to do more with less\nBrings new ideas constantly and can prioritize at a granular level\nIs resilient through changing priorities, new information, and mini-pivots\nGets excited by ownership, ambiguity, and wearing multiple hats\nWants to work in tight feedback loops with customers\nHas high schlep tolerance and is willing to do unglamorous work when it moves the business forward\nCan push back, think independently, and still move quickly\nPreferred experience\nApplied ML, data science, or AI systems work in production or near-production environments\nAttribution modeling, traffic analysis, forecasting, causal inference, experimentation, or product analytics\nExperience taking ML models from offline analysis to production systems customers actually use\nData pipelines, instrumentation, and signal collection from messy real-world sources\nStrong Python and SQL skills\nLLM workflows, retrieval systems, evals, fine-tuning, and model evaluation\nAI agents, including context management, orchestration, tool use, and evals\nEcommerce, marketplaces, search, recommendations, analytics, or growth systems\nEnough full-stack experience to ship customer-facing product, APIs, or internal tools when needed (Typescript, Express, React)\nWhy join\nYou’ll work on problems that sit between modeling, product, and data infrastructure.\nThe work is fast-paced, practical, and tied directly to company priorities. You will not spend months optimizing one narrow model in isolation.\nThis is a rare applied ML role where the work goes from messy data to production product to customer impact quickly. You’ll help decide what gets built, ship it end to end, and see whether it actually changes business outcomes.\nYou’ll be able to point to the models, systems, and product decisions you made as part of the reason why we win.\n1. Pending Approval\n2. Quick Call with Founder\n3. Engineering Challenge\n4. One to two-day work trial\n5. Offer Extended\n6. Hired","description_format":"text","description_chars":6083,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Marketplaces"],"lifecycle":[{"event":"open","at":"2026-09-25T20:43:38Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":3,"expected_fill_days":22,"reasons":["conf:1","win:early"],"computed_at":"2026-09-29T05:45:00Z"},"pay":{"stated_usd_annual":250000,"is_top_pay":true},"html_url":"https://alion.io/job/wildcard-founding-applied-ml-engineer","json_url":"https://alion.io/job/wildcard-founding-applied-ml-engineer.json","meta":{"generated_at":"2026-09-30T04:09:08Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2790,"day_limit":5000,"remaining_today":2210,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}