Applied Scientist / Applied ML Engineer

Job not on LinkedIn

🔥 6 minutes ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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

• End-to-End ML Ownership • Own end-to-end ML solutions for pricing, bidding, and risk decisioning. • Formulate model objectives from first principles, including loss functions, constraints, and metrics, and implement them as production-grade services. • Experimentation & Iteration • Design and run experiments, including A/B tests and offline evaluations, and iterate with clear success metrics. • Production Monitoring • Monitor models in production, investigate regressions, and continuously improve performance.

🎯 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.

🏖️ 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.