{"id":822073,"url":"https://alion.io/job/tolken-applied-scientist-applied-ml-engineer","title":"Applied Scientist / Applied ML Engineer","company":{"id":688907,"name":"Tolken","domain":"tolken.co","url":"https://alion.io/company/tolken","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US","IN"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"ETL/ELT","optional":false},{"name":"gRPC","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Recommender Systems","optional":true},{"name":"SHAP","optional":true}],"status":"live","first_seen_at":"2026-03-23T18:53:22Z","employer_posted_date":"2026-03-23","last_verified_at":"2026-10-17T06:35:08Z","board_verified":true,"closed_at":null,"days_open":207,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":207},"description":"The Role\nWe are looking for an Applied Scientist / Applied ML Engineer to design, build, and deploy machine learning models that power pricing, bidding, and decisioning on a cross-border payments platform. This role owns problems end to end, from formulation to production, and partners closely with Product and Backend Engineering.\nKey Responsibilities\nEnd-to-End ML Ownership\nOwn end-to-end ML solutions for pricing, bidding, and risk decisioning.\n\nFormulate model objectives from first principles, including loss functions, constraints, and metrics, and implement them as production-grade services.\n\nExperimentation & Iteration\nDesign and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics.\n\nProduction Monitoring\nMonitor models in production, investigate regressions, and continuously improve performance.\n\nRequirements\nEssential\n3-7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist in industry.\n\nBachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience.\n\nStrong Python skills, including pandas, NumPy, and scikit-learn, plus at least one of PyTorch, TensorFlow.\n\nStrong ML fundamentals, including supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics.\n\nExperience designing models from first principles and shipping them to production, in batch or real-time.\n\nHands-on experience with data pipelines and ETL, such as Airflow or Spark, and strong SQL for feature engineering.\n\nExperience integrating ML into REST or gRPC APIs and microservice architectures.\n\nAbility to design and interpret experiments with statistical rigor.\n\nStrong problem-solving and communication skills, and the ability to work effectively in cross-functional and distributed teams.\n\nNice to Have\nOptimization, bandits, or decision-making under uncertainty, including dynamic pricing and bid optimization.\n\nBidding, auctions, marketplace, or recommendation systems experience.\n\nFintech background, including payments, cross-border, lending, trading, or risk and scoring.\n\nFraud, AML, credit risk, or vendor risk scoring models.\n\nModel explainability tooling, including SHAP and feature importance, for auditable decisions.\n\nCloud experience (AWS, GCP, or Azure), Docker, and MLOps basics such as model registry and CI/CD.\n\nWhat We Offer\nReal ML in production with direct impact on pricing, risk, and vendor decisions at scale.\n\nOwnership of core models with room to influence architecture and roadmap.\n\nStrong engineering peers and complex optimization problems in a high-growth fintech.\n\nEqual Opportunities Statement\nTolken is an equal opportunity employer. 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