Senior Machine Learning Engineer

Finstock, Inc.; Northern (KY); Remote; USD 125,000 - 155,000; Full time

Benefits offered by this job

Remote work within the United States

Job summary

Finstock, Inc. is seeking a Senior Machine Learning Engineer to design, build, evaluate, deploy, and operate production ML systems across our financial research and market intelligence platform.

You will work on pipelines for data preparation, feature engineering, training, validation, inference, and monitoring, collaborating with AI, Data, and Software teams to productionize validated research and modeling approaches.

Qualifications

• 5+ years of professional ML engineering experience.
• Strong Python programming skills.
• Experience building production ML systems.
• Familiarity with cloud platforms (AWS/GCP/Azure).
• Proficient SQL and data tooling.

Responsibilities

• Design, develop, train, evaluate, deploy, and maintain production-grade ML systems.
• Build reliable ML pipelines for data prep, training, inference, monitoring.
• Collaborate with AI, Data, and Software Engineering teams.

Skills

Python

Production ML

SQL

API design

CI/CD

Education

Bachelor's degree in CS/Math/Related

Tools

PyTorch

TensorFlow

scikit-learn

XGBoost

LightGBM

Job description

# Senior Machine Learning EngineerDesign, build, evaluate, deploy, and operate production machine-learning systems for financial research and market intelligence. Remote within the United States, with periodic business travel.United StatesFull-timeUSD $125,000-$155,000/year## About Finstock, Inc.Finstock, Inc. builds AI-powered financial research, trading analytics, quantitative research, market intelligence, and research-support infrastructure for analysts, research teams, experienced market users, and institutions.Our products combine financial and market data, machine learning, quantitative analytics, AI-assisted research workflows, retrieval systems, and secure user-scoped infrastructure to help professional users organize and analyze complex financial information.## About the RoleFinstock is seeking a Senior Machine Learning Engineer to design, build, evaluate, deploy, and operate production machine-learning systems across our financial research and market intelligence platform.This role sits at the intersection of machine learning, software engineering, financial data, AI infrastructure, and production reliability. You will work on systems that support research intelligence, financial-document understanding, retrieval and ranking, market-data analysis, model evaluation, anomaly detection, classification, forecasting, and AI-assisted workflows.You will be expected to take machine-learning systems beyond experimentation. This includes building reliable training and inference pipelines, designing evaluation frameworks, improving model performance and efficiency, implementing monitoring and observability, and integrating models into scalable production applications.You will collaborate closely with AI Engineers, Data Engineers, Software Engineers, Quantitative Researchers, Product teams, and financial research teams to translate complex research problems into measurable and maintainable machine-learning systems.This is a remote full-time role based in the United States. Periodic business travel is required based on project, team, client, conference, planning, or company-offsite needs.## Key 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, financial-document intelligence, and other research-oriented use cases.* Build machine-learning services and APIs that can be integrated into Finstock's 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.* Establish benchmarks, evaluation datasets, regression tests, and acceptance criteria for model changes.* Improve model quality, generalization, robustness, inference speed, computational efficiency, and production stability.* Develop feature pipelines using financial market data, company fundamentals, filings, macroeconomic indicators, research metadata, text data, and other approved datasets.* Work with embeddings, retrieval, ranking, search, and representation-learning systems where appropriate.* Build and improve systems supporting AI-assisted financial research, including retrieval, reranking, classification, evaluation, and model-quality workflows.* Detect and mitigate model risks including overfitting, data leakage, look-ahead bias, survivorship bias, dataset shift, model drift, and weak evaluation methodology.* Implement model monitoring for data quality, prediction distributions, drift, latency, failures, and performance degradation.* Collaborate with Data Engineering teams on feature pipelines, training datasets, data lineage, validation, and scalable data infrastructure.* Collaborate with Software Engineering teams to integrate ML capabilities into APIs, backend services, dashboards, and user-facing products.* Collaborate with Quantitative Researchers and Data Scientists to productionize validated research and modeling approaches.* Collaborate with AI Engineers on LLM-related systems, retrieval workflows, model evaluation, embeddings, and agentic research infrastructure where relevant.* Participate in system architecture, technical planning, code reviews, incident response, and engineering-design discussions.* Improve ML infrastructure, development standards, testing practices, documentation, and deployment workflows.* Mentor engineers or contribute technical guidance to less-experienced team members where appropriate.* Maintain clear documentation covering model architecture, datasets, assumptions, evaluation, limitations, dependencies, and operational procedures.* Ensure machine-learning systems follow Finstock's security, privacy, confidentiality, data-licensing, access-control, and responsible-AI requirements.## Required Qualifications* 5+ years of professional experience in machine learning engineering, applied machine learning, ML infrastructure, AI engineering, or a closely related technical role.* Strong proficiency in Python.* Strong software-engineering fundamentals, including clean architecture, testing, debugging, version control, API design, and production reliability.* Experience building and operating machine-learning systems in production.* Strong knowledge of supervised and unsupervised machine-learning methods.* Strong understanding of statistics, probability, model evaluation, experimentation, and generalization.* Experience with common machine-learning frameworks and libraries such as: PyTorch; TensorFlow; scikit-learn; XGBoost; LightGBM; or comparable tools;* Experience building training, validation, inference, and monitoring pipelines.* Experience working with large structured, semi-structured, or unstructured datasets.* Strong SQL skills and experience interacting with production data systems.* Experience exposing models through APIs, batch pipelines, or online inference services.* Understanding of feature engineering, model selection, hyperparameter optimization, cross-validation, and production evaluation.* Ability to identify and mitigate data leakage, overfitting, distribution shift, and other common ML failure modes.* Familiarity with cloud infrastructure such as AWS, Google Cloud Platform, Microsoft Azure, or equivalent platforms.* Experience with Git, GitHub, CI/CD, containerization, testing, and collaborative engineering workflows.* Strong debugging and root-cause-analysis skills.* Strong written communication and technical-documentation ability.* Ability to collaborate effectively across engineering, product, research, and quantitative teams.* Professional commitment to security, privacy, confidentiality, and responsible handling of financial and user-related data.* Authorization to work in the United States under the employment arrangement applicable to the role.* Willingness and ability to undertake periodic business travel.## Preferred Qualifications* Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Data Science, Financial Engineering, or another quantitative discipline.* Experience with fintech, financial services, capital markets, market intelligence, quantitative research, investment research platforms, or financial-data products.* Experience with financial datasets including: equities; ETFs; indices; FX; commodities; financial statements; filings; corporate actions; macroeconomic indicators; market-data APIs;* Experience with NLP, embeddings, information retrieval, ranking, recommendation, or semantic-search systems.* Experience with vector databases or vector-search infrastructure.* Experience developing retrieval-augmented generation or LLM-supporting ML systems.* Experience evaluating LLMs or AI-assisted workflows for factuality, retrieval quality, ranking quality, reliability, or financial reasoning.* Experience with time-series modeling, anomaly detection, forecasting, or regime analysis.* Experience with distributed training or distributed data-processing systems.* Experience with MLflow, model registries, experiment tracking, feature stores, or MLOps platforms.* Experience with Kubernetes, Docker, Terraform, GitHub Actions, or similar infrastructure tooling.* Familiarity with Spark, Databricks, Snowflake, BigQuery, Redshift, or similar analytical infrastructure.* Experience with model serving technologies and high-throughput inference systems.* Familiarity with model explainability, responsible AI, model governance, and model-risk management.* Experience building permission-aware, user-scoped, or enterprise-grade machine-learning systems.* Experience mentoring engineers or leading technically complex ML projects.## What You Will Work OnYou may work on projects such as:* Machine-learning systems that organize and classify financial research information.* Ranking and retrieval models for financial documents, market context, and research evidence.* Search and semantic-retrieval infrastructure supporting Findex and Finstock Research OS.* Models for detecting anomalies across financial, market, operational, or product datasets.* Financial-document classification, extraction, and relevance-ranking systems.* Embedding pipelines and vector-search systems for research workflows.* ML-based source relevance and research-quality systems.* Model evaluation infrastructure for AI-generated financial research.* Market and financial-data feature pipelines used by analytical systems.* Forecasting, classification, and regime-detection research where appropriate.* Production inference APIs and internal machine-learning services.* Model monitoring, drift detection, and quality-control infrastructure.* Machine-learning experimentation and deployment tooling used across Finstock engineering teams.* AI-assisted research workflows combining machine learning, retrieval, financial data, and LLM systems.## Machine Learning Quality StandardsBecause Finstock operates in financial research and data-intensive environments, production models must be evaluated beyond basic predictive performance.Senior Machine Learning Engineers are expected to consider:* Data quality and provenance* Train/validation/test integrity* Data leakage* Look-ahead bias* Survivorship bias* Dataset imbalance* Overfitting* Distribution shift* Model drift* Feature instability* Calibration* Robustness* Latency* Cost* Failure behavior* Monitoring* Reproducibility* Explainability where appropriate* Security and privacy constraints* Appropriate human reviewModel limitations must be documented clearly and communicated to relevant stakeholders.## Production and MLOps ResponsibilitiesYou may contribute to systems covering:* Model training pipelines* Model registries* Experiment tracking* Feature engineering pipelines* Feature stores* CI/CD for machine learning* Automated model testing* Shadow deployments* Model versioning* Online and batch inference* Model monitoring* Data and concept drift detection* Performance dashboards* Rollback mechanisms* Model-quality alerts* Resource and inference-cost optimizationProduction deployment should include appropriate testing, monitoring, observability, and rollback planning.## CollaborationYou will work closely with:AI Engineering: On retrieval, embeddings, LLM evaluation, ranking, research agents, and AI-assisted financial workflows.Data Engineering: On training datasets, feature pipelines, data quality, lineage, storage, and scalable data infrastructure.Software Engineering: On APIs, model-serving infrastructure, backend integration, reliability, and user-facing product systems.Data Science: On experimentation, statistical validation, feature development, and model evaluation.Quantitative Research: On financial hypotheses, market-data analysis, model diagnostics, and quantitative research workflows.Product and Research Teams: On translating financial research requirements into measurable machine-learning capabilities.## Travel RequirementPeriodic business travel is required.Travel may include:* Company planning sessions* Engineering and research offsites* Cross-functional workshops* Industry conferences* Partner or vendor meetings* Enterprise or institutional meetings where technically relevant* Other approved company activitiesTravel frequency will depend on business and project requirements and will be communicated in advance where practical.Approved business-related travel expenses will be handled in accordance with Finstock company policy.Travel requirements do not imply continuous or weekly travel.## What We Offer* Annual base salary of USD $125,000-$155,000, depending on experience, technical depth, location, and role fit.* Full-time remote work within the United States.* Opportunity to build production ML infrastructure for an AI-powered financial research and market intelligence platform.* Direct collaboration with AI, Data, Quantitative Research, Software Engineering, Product, and financial research teams.* Exposure to machine learning, financial market data, retrieval systems, quantitative research, and production AI systems.* Opportunity to influence Finstock's ML architecture, engineering standards, evaluation methodology, and production infrastructure.* Access to approved development, cloud, research, and productivity tools required for the role.* Approved business-related travel handled in accordance with company policy.## Important Role BoundariesThis is a machine-learning engineering role supporting financial research, analytics, AI systems, and production technology infrastructure.The role does not require or permit the employee to:* Provide personalized investment, legal, tax, accounting, or financial advice to users or clients.* Recommend that an individual buy, sell, or hold a financial instrument based on personal circumstances.* Execute trades or manage client funds.* Handle client assets, deposits, withdrawals, or payments.* Present machine-learning predictions, model outputs, forecasts, backtests, or statistical relationships as guaranteed future outcomes.* Deploy unvalidated models into production outside approved engineering and review procedures.* Use unauthorized datasets or violate third-party data licensing terms.* Access user or company information beyond approved role-based permissions.* Bypass required security, privacy, audit, access-control, or model-governance controls.* Conceal material model limitations or known quality issues.* Request applicants to pay application fees, recruitment fees, training payments, software payments, equipment payments, travel deposits, or onboarding fees.## Location and Work EligibilityThis position is remote within the United States.Candidates must be legally authorized to work in the United States under the employment arrangement applicable to the role.Unless separately stated in writing, the role posting does not guarantee immigration sponsorship or relocation assistance.Candidates must also be able to undertake required business travel subject to applicable travel-document, immigration, security, and company-policy requirements.## Ideal CandidateThe ideal candidate combines machine-learning depth with strong software-engineering judgment.You should be equally comfortable:* experimenting with a new modeling approach;* challenging an evaluation methodology;* reviewing training data quality;* building a reliable inference service;* debugging production behavior;* designing monitoring;* discussing architecture with engineers;* explaining model limitations to product and research teams.You should care not only about whether a model performs well in a notebook, but whether it is:* reproducible;* maintainable;* observable;* secure;* scalable;* appropriately validated;* useful under changing conditions;* safe to operate in a financial-research environment.## Equal Opportunity StatementFinstock, Inc. considers qualified applicants based on role-related skills, experience, technical ability, work quality, availability, business requirements, and applicable employment requirements.Finstock, Inc. does not make hiring decisions based on age, gender, gender identity, religion, ethnicity, race, national origin, disability, sexual orientation, marital status, family status, veteran status, or any other status protected by applicable law.Finstock, Inc. does not request application fees, recruitment fees, training payments, software payments, equipment payments, travel deposits, or onboarding fees from candidates.Role Application## Apply for this roleSubmit your information for the Senior Machine Learning Engineer role.Open## Role summaryCompany: Finstock, Inc.Location: Remote - United StatesEmployment: Full-timeBase salary: USD $125,000-$155,000/year, depending on experience, technical depth, location, and role fitReporting to: Engineering Lead / AI Engineering LeadershipTravel: Periodic business travel required for company meetings, technical planning, team offsites, industry events, and cross-functional collaborationCollaboration: AI Engineering, Data Engineering, Software Engineering, Quantitative Research, Product, and Research teamsApply now