{"id":14053,"url":"https://alion.io/job/ramp-senior-applied-scientist-credit-risk","title":"Machine Learning Engineer","company":{"id":4342,"name":"Ramp","domain":"ramp.com","url":"https://alion.io/company/ramp","size_band":"501-1000","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":87,"open_postings":48,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":71,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":330000,"currency":"USD","period":"year","gross":null,"usd_annual":330000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"dbt","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-05-11T15:17:51Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-09-24T13:54:15Z","board_verified":true,"closed_at":null,"days_open":135,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":135},"description":"About Ramp\nRamp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.\nThe problems are high-stakes, data-dense, and unforgiving.\nWe hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.\nThe median Ramp customer saves 5% and grows revenue 16% in their first year - far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.\nIf you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.\nAbout the Role\nWe’re seeking someone to lead the future of fraud machine learning at Ramp. In this role, you will help build core machine learning models, design data architectures, and set strategic roadmaps to help Ramp mitigate fraud-related threats while minimizing the friction experience by legitimate users. You will partner closely with product and engineering counterparts across model design, implementation, execution, and analysis.\nWhat You’ll Do\nEmploy statistical and machine learning techniques on large datasets to discover patterns of fraud, platform abuse, and identity theft\n\nPrototype and productionize machine learning models and rules-based systems to protect Ramp and its users from fraud\n\nPartner closely with Fraud Engineering and Data Platform teams to augment and leverage data across first and third party sources, ensuring we’ve added as much context as possible to every decision we make\n\nContribute to the culture of Ramp’s machine learning team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way\n\nWhat You Need\nBachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields\n\nA minimum of 5 years of industry experience as a Machine Learning Engineer, Applied Scientist or Data Scientist\n\nStrong python experience (numpy, pandas, sklearn, pytorch etc.) across ML techniques and backend engineering\n\nPrior experience deploying Machine Learning models to production and making meaningful contribution to backend systems\n\nStrong knowledge of SQL (Snowflake, Postgres, etc.)\n\nFluency with agentic (AI) tools for software development and data analysis\n\nAbility to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions\n\nNice-to-Haves\nPhD in Math, Economics, Physics, Computer Science, or other quantitative fields\n\nContext on Fraud and/or Identity Threat detection systems\n\nExperience at a high-growth startup\n\nExperience with the modern data stack ( Snowflake / Hex / dbt / RisingWave / etc )\n\nStrong perspective on data science + ML engineering development cycle, especially in a post-AI setting\n\nExperience developing LLM-backed systems or tools\n\nBenefits available to all full-time Ramp employees (Global)\nFlexible PTO\n\nCentralized home-office equipment ordering\n\nHealth and wellness stipend\n\nBudget for intra-office travel\n\nWeekly coffee stipend\n\nUnited States\n100% medical, dental & vision insurance coverage for you, with partial coverage for dependents\n\nOne Medical annual membership\n\n401(k), including employer match on contributions made while employed by Ramp\n\nFertility HRA (up to $10,000 per year)\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay\n\nPet insurance\n\nIn-office perks: lunch, snacks, drinks, and more\n\nRelocation expense coverage to NYC or SF (if needed)\n\nCanada\nGroup medical, dental, and vision coverage through Sun Life\n\nLife, AD&D, and disability coverage\n\nFertility drug coverage (up to $4,000 lifetime)\n\nGroup Retirement Plan with employer match (RRSP + DPSP)\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay\n\nEmployee Assistance Program and virtual care through Lumino Health\n\nUnited Kingdom\nPrivate medical insurance through Freedom Elite\n\nVirtual GP and at-home care via eMed x Livi\n\nWorkplace pension through Penfold, with salary sacrifice option\n\nParental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay\n\nReferral Instructions\nIf you are being referred for the role, please contact that person to apply on your behalf.\nOther notices\nPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.\nBeware of recruiting scams: Ramp will only contact you through official @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.\nRamp Applicant Privacy Notice","description_format":"text","description_chars":5076,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Health insurance","Insurance coverage","Parental leave","Retirement plans","Vision insurance"],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":["Financial Services"],"lifecycle":[{"event":"open","at":"2026-05-11T15:17:51Z"},{"event":"close","at":"2026-08-04T20:28:58Z"},{"event":"reopen","at":"2026-09-23T23:00:19Z"}],"liveness":{"score":26,"band":"fade","label":"Fading","p_open":1,"p_active":0.723,"p_room":0.36,"age_days":135,"expected_fill_days":71,"reasons":["conf:2","velocity","win:tail","crowd:brand"],"computed_at":"2026-09-24T05:45:00Z"},"pay":{"stated_usd_annual":330000,"is_top_pay":true},"html_url":"https://alion.io/job/ramp-senior-applied-scientist-credit-risk","json_url":"https://alion.io/job/ramp-senior-applied-scientist-credit-risk.json","meta":{"generated_at":"2026-09-24T14:54:32Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}