Senior Machine Learning Engineer

Finstock, Inc.; United States; Remote; USD 125,000 - 155,000; Full time

Job summary

Finstock, Inc. seeks a design-focused Machine Learning Engineer to design, build, evaluate, deploy, and operate production ML systems powering a financial research and market intelligence platform. The role is remote within the United States with periodic travel for offsites and partner meetings.

You will build scalable ML pipelines, APIs, and services across research, analytics, and market-intelligence products with emphasis on reliability, performance, and cost controls.

Qualifications

• Five+ years of professional experience in machine learning engineering or similar.
• Strong proficiency in Python and core software engineering fundamentals.
• Demonstrated experience building and operating ML systems in production.
• Knowledge of supervised/unsupervised methods, statistics and model evaluation.
• Experience with ML frameworks and large datasets.

Responsibilities

• Design, develop, train, evaluate, deploy, and maintain production-grade ML systems.
• Build reliable ML pipelines covering data prep, feature engineering, training, validation, inference, monitoring, and retraining.
• Develop models for classification, ranking, retrieval, anomaly detection, forecasting, and financial-document intelligence.
• Build ML services and APIs integrated into research, analytics, and market-intelligence products.
• Create scalable batch and online inference workflows with latency, reliability, and cost controls.
• Design robust model evaluation frameworks using offline and production metrics, benchmarks, and regression tests.
• Build feature pipelines from financial market data, filings, macro indicators, research metadata, and text data.

Skills

Python

Production ML

Software engineering

ML theory & statistics

SQL

Embeddings & retrieval concepts

Tools

PyTorch

TensorFlow

scikit-learn

XGBoost

LightGBM

Job description

Role overview

Design, build, evaluate, deploy, and operate production machine learning systems that power a financial research and market intelligence platform. The role sits at the intersection of machine learning, software engineering, financial data, and production reliability, supporting research intelligence, financial-document understanding, retrieval and ranking, market-data analysis, anomaly detection, classification, and forecasting. The position is remote within the United States, with periodic business travel for offsites, workshops, conferences, and partner meetings.

Responsibilities

• Design, develop, train, evaluate, deploy, and maintain production-grade machine learning systems.
• Build reliable ML pipelines spanning data preparation, feature engineering, training, validation, inference, monitoring, and retraining.
• Develop models for classification, ranking, retrieval, anomaly detection, forecasting, and financial-document intelligence.
• Build machine learning services and APIs integrated into research, analytics, and market-intelligence products.
• Develop scalable batch and online inference workflows with appropriate latency, reliability, and cost controls.
• Design rigorous model evaluation frameworks using statistically appropriate offline and production metrics, including benchmarks, regression tests, and acceptance criteria.
• Build feature pipelines from financial market data, company fundamentals, filings, macroeconomic indicators, research metadata, and text data.

Requirements

• Five or more years of professional experience in machine learning engineering, applied ML, ML infrastructure, or AI engineering.
• Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, debugging, version control, API design, and production reliability.
• Demonstrated experience building and operating machine learning systems in production.
• Strong knowledge of supervised and unsupervised methods, plus statistics, probability, model evaluation, experimentation, and generalization.
• Experience with frameworks such as PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable tools.
• Strong SQL skills and experience with large structured, semi-structured, or unstructured datasets.
• Familiarity with embeddings, retrieval, ranking, search, and representation-learning systems.

Nice to have

• Experience with time-series, financial market data, quantitative research workflows, or financial document analysis.
• Background that combines ML depth with strong software engineering judgment and cross-functional collaboration with product and research teams.

Benefits and work setup

• Annual base salary of USD $125,000 to $155,000, depending on experience, technical depth, location, and role fit.
• Full-time remote work within the United States.
• Periodic business travel for planning sessions, offsites, cross-functional workshops, conferences, and partner or institutional meetings, with approved expenses handled per company policy.