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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Sr. - WFH - **Company:** TRENT HOEK OUTDOORS LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $125,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Multitier Architecture, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing, Code Review, Encodings, Continuous Integration, Learning Management Systems, Data Cleansing, Information Engineering, Data Infrastructure, Information Leak Prevention, Database Queries, Software Debugging, Distributed Computing Environment, Distributed Data Store, Document Retrieval, Github, Information Retrieval, Python (Programming Language), Machine Learning, Natural Language Processing, Operational Databases, Regression Testing, Tensorflow, Search Technologies, Software Deployment, Software Engineering, Systems Architecture, Unstructured Data, Management of Software Versions, AI Infrastructure, Feature Store, Google Cloud, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Snowflake, Apache Spark, Model Validation, Backend, Agentic-AI, Git, Containerization, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Low Latency, Xgboost, Build Tools, Machine Learning Operations, Drift Detection, Evaluation of Large Language Models, Api Design, Model Explainability, Terraform, Software Version Control, Docker, Amazon Redshift, Databricks - **Published:** October 20, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pszbdjxs4s ## About the Role * At least five 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 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 machine-learning methods. * Solid understanding of statistics, probability, experimentation, model evaluation, and generalization. * Experience with 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 working with production data systems. * Experience deploying 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 address data leakage, overfitting, distribution shift, and other common machine-learning 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, root-cause analysis, written communication, and technical documentation skills. * Ability to collaborate effectively across engineering, product, research, and quantitative teams. * 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., * Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Data Science, Financial Engineering, or another quantitative discipline. * Experience in fintech, financial services, capital markets, market intelligence, quantitative research, investment research platforms, or financial-data products. * Experience with financial datasets, including equities, ETFs, indices, foreign exchange, commodities, financial statements, regulatory filings, corporate actions, macroeconomic indicators, or market-data APIs. * Experience with natural language processing, embeddings, information retrieval, ranking, recommendation, or semantic search. * Experience with vector databases or vector-search infrastructure. * Experience developing retrieval-augmented generation systems or machine-learning components supporting large language models. * Experience evaluating LLMs and AI-assisted workflows for factual accuracy, retrieval quality, ranking, reliability, or financial reasoning. * Experience with time-series modeling, anomaly detection, forecasting, or regime analysis. * Experience with distributed training or distributed data-processing systems. * Familiarity with MLflow, model registries, experiment tracking, feature stores, or MLOps platforms. * Experience with Kubernetes, Docker, Terraform, GitHub Actions, or similar infrastructure tools. * Familiarity with Spark, Databricks, Snowflake, BigQuery, Redshift, or comparable analytical infrastructure. * Experience with model-serving technologies and high-throughput inference systems. * Knowledge of 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., Candidates must be authorized to work in the United States under the applicable employment arrangement and must be able to meet the periodic travel requirements. Immigration sponsorship and relocation assistance are not guaranteed unless separately confirmed in writing. ## Description Finstock, Inc. is seeking a Senior Machine Learning Engineer to design, develop, evaluate, deploy, and operate production machine-learning systems across its financial research and market intelligence platform. This role combines machine learning, software engineering, financial data, AI infrastructure, and production reliability. You will build systems for financial-document understanding, retrieval and ranking, market-data analysis, anomaly detection, classification, forecasting, and AI-assisted research. Working closely with AI Engineers, Data Engineers, Software Engineers, Quantitative Researchers, Product teams, and financial research teams, you will turn research requirements into reliable, measurable, and maintainable production solutions., * Design, develop, train, evaluate, deploy, and maintain production-grade machine-learning systems. * Build reliable ML pipelines covering 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 applications. * Build machine-learning services and APIs that integrate with Finstock's research, analytics, and market intelligence products. * Develop scalable batch and online inference workflows with appropriate latency, reliability, and cost controls. * Design statistically sound model-evaluation frameworks, including benchmarks, evaluation datasets, regression tests, and acceptance criteria. * Improve model quality, generalization, robustness, inference speed, computational efficiency, and production stability. * Develop feature pipelines using approved financial market data, company fundamentals, filings, macroeconomic indicators, research metadata, and text datasets. * Build and improve embedding, retrieval, ranking, search, and representation-learning systems where appropriate. * Support AI-assisted financial research through retrieval, reranking, classification, and model-quality evaluation workflows. * Identify and mitigate model risks, including overfitting, data leakage, look-ahead bias, survivorship bias, dataset shift, and model drift. * Implement monitoring for data quality, prediction distributions, latency, failures, drift, and performance degradation. * Collaborate with Data Engineering on training datasets, feature pipelines, data lineage, validation, and scalable data infrastructure. * Work with Software Engineering to integrate ML capabilities into APIs, backend services, dashboards, and user-facing products. * Partner with Quantitative Researchers and Data Scientists to productionize validated research and modeling approaches. * Collaborate with AI Engineers on LLM-related systems, embeddings, retrieval workflows, model evaluation, and agentic research infrastructure where relevant. * Contribute to system architecture, technical planning, code reviews, incident response, and engineering-design discussions. * Improve ML infrastructure, testing practices, development standards, documentation, and deployment workflows. * Provide technical guidance or mentorship to less-experienced engineers where appropriate. * Document model architecture, datasets, assumptions, evaluation methods, limitations, dependencies, and operational procedures. * Ensure systems comply with security, privacy, confidentiality, data-licensing, access-control, and responsible-AI requirements., The role may involve building financial research classification systems, document retrieval and ranking models, semantic-search infrastructure, financial-document extraction systems, anomaly detection models, embedding pipelines, research-quality evaluation tools, forecasting systems, and production inference APIs. You may also contribute to ML infrastructure for model training, experiment tracking, automated testing, versioning, deployment, monitoring, drift detection, rollback planning, and inference-cost optimization. Machine Learning Quality & Production Standards Production systems must be evaluated beyond basic predictive performance. You will be expected to consider data quality and provenance, training and evaluation integrity, data leakage, look-ahead and survivorship bias, dataset imbalance, overfitting, distribution shift, calibration, feature stability, model drift, latency, cost, robustness, reproducibility, and appropriate human review. Model limitations must be documented and communicated clearly. Production deployments should include appropriate testing, monitoring, observability, and rollback planning. Collaboration You will work closely with AI Engineering on retrieval, embeddings, LLM evaluation, ranking, and AI-assisted research; Data Engineering on datasets, feature pipelines, data quality, and infrastructure; Software Engineering on APIs, model serving, and product integration; Data Science and Quantitative Research on experimentation, statistical validation, and financial modeling; and Product and Research teams on translating business needs into measurable machine-learning capabilities.