Intelligent Audit

Compensation

Full Description

Job Title: Senior Machine Learning Engineer Reports to: Lead Machine Learning Engineer Intelligent Audit is a fast-growing freight audit & business analytics technology company helping our customers become smarter shippers - shipping to their customers faster, cheaper, and with less delivery exceptions. We use big data to help our customers remove inefficiencies in their global transportation spend.  As a Senior Machine Learning Engineer within the Data Science organization, you will design, build, and maintain production-grade ML solutions on large logistics, shipping, and billing datasets. You will own models and services end-to-end (exploration and prototyping through deployment, monitoring, and continuous improvement), while collaborating closely with data science, product, engineering, and operations teams. What You Will Do:  Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. The individual with this position in our company will be expected, on a regular basis, to: *  ML design and development Architect, implement, and maintain ML models (e.g., gradient boosted trees, deep learning, forecasting, transformers) using Python and its data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter, visualization libraries). * Data analysis and feature engineering Explore, visualize, and analyze large internal and external datasets, especially structured multivariate time-series, logistics, and billing data, to engineer features, validate assumptions, and improve model performance. * Production systems and APIs Develop robust, well-structured code and internal APIs (e.g., FastAPI) for online and batch inference, integrating ML services into existing systems and workflows. * Code quality and engineering practices Apply software engineering best practices including version control, code reviews, documentation, and test-driven development to ensure clarity, reliability, and maintainability. * MLOps, CI/CD, and observability Design and operate CI/CD pipelines (e.g., GitHub Actions) for ML workloads; build and maintain containerized deployments (Docker, Kubernetes or similar); instrument experiment tracking and monitoring using tools such as MLflow, TensorBoard, Datadog, Neptune, or Weights & Biases.   * Data engineering collaboration Work with relational and analytical data stores (Postgres, parquet, DuckDB) and collaborate with data engineering on SQL- and dbt-based pipelines that support training, validation, and production scoring. * LLM integration Use LLM APIs and tools (e.g., OpenAI, Cursor) and prompting strategies to integrate LLMs into products, workflows, and data pipelines where they provide clear business value. * Lifecycle ownership and continuous improvement Own the ML lifecycle: problem framing, data exploration, modeling, evaluation, deployment, monitoring, retraining, and decommissioning; identify opportunities to reduce technical debt and improve performance and reliability. * Collaboration, communication, and mentorship Translate complex ML and statistical concepts into clear language for technical and non-technical stakeholders; document and present findings and design decisions; provide guidance and feedback to junior data scientists and engineers. What You Will Bring: * Strong analytical and problem-solving skills with a focus on machine learning and data-driven decision making. * Advanced Python proficiency and deep familiarity with data science libraries and tooling (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter). * Experience with deep learning and decision tree–based methods, including production use of models such as gradient boosted trees and neural networks. * Proven experience working with structured multivariate time-series and other large structured datasets. * Proficiency with Linux, Git, Bash, and working in cloud or remote high-performance computing environments for big data and large-scale training. * Hands-on experience with CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes or similar) for ML workloads. * Experience with monitoring, logging, and experiment tracking for ML systems (e.g., MLflow, TensorBoard, Datadog, Neptune, Weights & Biases). * Comfort working with SQL and relational databases (Postgres) as well as analytical formats and engines (parquet, DuckDB), and collaborating with data engineering on dbt or similar tooling. * Experience with LLM APIs, integration patterns, and prompting strategies for LLM-powered workflows and applications. * Strong written and verbal communication skills and a track record of successful collaboration with cross-functional stakeholders. * Interest in mentoring and helping level up team members on ML, MLOps, and software engineering best practices.   Minimum Qualifications * Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Physics, Statistics, or a related quantitative field, or equivalent practical experience demonstrating senior-level ML engineering capability. * At least 5 years of professional Python development experience focused on data science and ML libraries. * At least 3 years of experience delivering end-to-end ML solutions in production (from exploration through deployment and monitoring). * At least 3 years of experience with deep learning and decision tree–based methods. * At least 2 years of experience working with structured multivariate time-series data. * Demonstrated experience with: * CI/CD pipeline design and operation for ML workloads. * Containerization (Docker) and orchestration (Kubernetes or similar). * Linux-based cloud and/or remote high-performance computing environments.   Preferred Characteristics: * Ph.D. or equivalent research experience focusing on cutting-edge ML techniques. * Experience with logistics, freight, or broader supply chain datasets and use cases. * Deep expertise in one or more of: transformer architectures (including for structured data), unsupervised learning on structured data, or advanced forecasting methods. * Track record of publications, conference talks, or notable open source / code portfolio demonstrating ML innovation. * Experience building and operating LLM-powered applications and tools (e.g., via OpenAI APIs, Cursor, or similar frameworks). * Track record of publications, conference talks, or notable open source / code portfolio demonstrating ML innovation. * Experience building and operating LLM-powered applications and tools (e.g., via OpenAI APIs, Cursor, or similar frameworks).