Applied Scientist / Applied ML Engineer

Job not on LinkedIn

🕒 July 28

🇮🇳 India – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 39%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Tolken

WebsiteLinkedInAll Job Openings

2 - 10 employees

Founded 2025

💳 Fintech

🔌 API

🤝 B2B

Fintech • API • B2B

Tolken is a technology provider that operates a network for coordinating global cross-border payments in real time. It connects banks, PSPs, FX desks, and liquidity providers via a single integration/API, offering intelligent routing, parallel provider checks, real-time tracking, full audit trails, and automated entity assessment to replace one-off enhanced due diligence. Tolken positions itself as infrastructure beneath global payment rails (in beta), enabling senders to access corridors without pre-funding or new licenses and enabling providers to receive routed volume with standardized, verified KYC/AML data. It explicitly states it is a technology provider and does not itself provide payment or other financial services.

📋 Description

• Design, build, and deploy machine learning models powering pricing, bidding, and decisioning on a cross-border payments platform • Own end-to-end ML solutions for pricing, bidding, and risk decisioning • Formulate model objectives from first principles, including loss functions, constraints, and metrics • Implement models as production-grade services • Design and run A/B tests and offline evaluations • Iterate using clear success metrics • Monitor models in production and investigate regressions • Continuously improve model performance • Partner closely with Product and Backend Engineering • Influence architecture and roadmap for core models

🎯 Requirements

• 3–7 years of experience as an ML Engineer, Applied Scientist, or Data Scientist in industry • Bachelor's or Master's in Computer Science, Machine Learning, Mathematics, Statistics, or equivalent practical experience • Strong Python skills, including pandas, NumPy, and scikit-learn, plus at least one of PyTorch, TensorFlow • Strong ML fundamentals, including supervised and unsupervised learning, model evaluation, regularization, feature engineering, and statistics • Experience designing models from first principles and shipping them to production, in batch or real-time • Hands-on experience with data pipelines and ETL, such as Airflow or Spark, and strong SQL for feature engineering • Experience integrating ML into REST or gRPC APIs and microservice architectures • Ability to design and interpret experiments with statistical rigor • Strong problem-solving and communication skills, and the ability to work effectively in cross-functional and distributed teams • Optimization, bandits, or decision-making under uncertainty, including dynamic pricing and bid optimization • Bidding, auctions, marketplace, or recommendation systems experience • Fintech background, including payments, cross-border, lending, trading, or risk and scoring • Fraud, AML, credit risk, or vendor risk scoring models • Model explainability tooling, including SHAP and feature importance, for auditable decisions • Cloud experience (AWS, GCP, or Azure), Docker, and MLOps basics such as model registry and CI/CD

🏖️ Benefits

• Real ML in production with direct impact on pricing, risk, and vendor decisions at scale • Ownership of core models with room to influence architecture and roadmap • Strong engineering peers and complex optimization problems in a high-growth fintech • Inclusive environment committed to equal opportunities