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Ref: #71494

ML Engineer

Senior Machine Learning Engineer

Join a team focused on building production-grade machine learning systems that power data-driven decision-making across large, complex datasets. In this role, you will own ML models and services end-to-end—from exploration and prototyping through deployment, monitoring, and continuous improvement—while collaborating closely with data science, product, engineering, and operations teams.

What You’ll Do

ML Design & Development

  • Architect, implement, and maintain machine learning models—including gradient-boosted trees, neural networks, forecasting models, and transformers.

  • Use Python and the modern data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter, visualization tools).

Data Analysis & Feature Engineering

  • Explore and analyze large structured datasets, particularly multivariate time-series and billing/operational data.

  • Engineer high-quality features, assess data assumptions, and iterate to improve model performance.

Production Systems & APIs

  • Develop clean, scalable code and internal APIs (e.g., FastAPI) for both online and batch inference.

  • Integrate ML services into existing systems and workflows.

Code Quality & Engineering Practices

  • Apply best practices in version control, documentation, code reviews, and test-driven development.

  • Ensure reliability, clarity, and long-term maintainability of ML codebases.

MLOps, CI/CD & Observability

  • Design and manage CI/CD pipelines for ML workloads (e.g., GitHub Actions).

  • Build and maintain containerized deployments using Docker and Kubernetes (or similar tools).

  • Implement monitoring, logging, and experiment tracking with platforms such as MLflow, TensorBoard, Datadog, Neptune, or Weights & Biases.

Data Engineering Collaboration

  • Work with relational and analytical data stores (Postgres, parquet, DuckDB).

  • Partner with data engineering teams on SQL/dbt-based pipelines for training, validation, and production scoring.

LLM Integration

  • Use LLM APIs and tooling (e.g., OpenAI, Cursor) to integrate large language models into products, workflows, and pipelines where they deliver measurable value.

Lifecycle Ownership & Continuous Improvement

  • Own the entire ML lifecycle: problem framing, exploration, modeling, evaluation, deployment, monitoring, retraining, and decommissioning.

  • Identify technical debt and drive ongoing improvements in performance and reliability.

Collaboration, Communication & Mentorship

  • Communicate complex ML concepts to both technical and non-technical audiences.

  • Document findings and architectural decisions.

  • Mentor junior data scientists and engineers to elevate team capabilities.

What You’ll Bring

  • Strong analytical and problem-solving skills grounded in machine learning principles.

  • Advanced Python expertise and deep knowledge of the data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter).

  • Experience deploying tree-based models and deep learning models in production.

  • Hands-on experience with structured multivariate time-series data.

  • Proficiency with Linux, Git, Bash, and cloud or high-performance computing environments.

  • Experience with CI/CD pipelines, Docker, and Kubernetes for ML workloads.

  • Familiarity with experiment tracking, monitoring, and logging tools for ML systems.

  • Comfort working with SQL, relational databases (e.g., Postgres), and analytical formats/engines (parquet, DuckDB).

  • Experience integrating and prompting LLM APIs for data and workflow automation.

  • Strong written and verbal communication skills and a track record of effective cross-functional collaboration.

  • Interest in mentoring others and improving engineering/ML practices across the team.

Minimum Qualifications

  • Bachelor’s, Master’s, or Ph.D. in a quantitative discipline—or equivalent experience demonstrating senior-level ML engineering capability.

  • 5+ years of professional Python development focused on data science and ML.

  • 3+ years building and deploying end-to-end ML solutions in production.

  • 3+ years working with deep learning or decision-tree-based methods.

  • 2+ years working with structured multivariate time-series datasets.

  • Demonstrated experience with:

    • CI/CD for ML workloads

    • Docker and Kubernetes (or similar orchestration)

    • Linux-based cloud or high-performance training environments

Preferred Qualifications

  • Ph.D. or equivalent research experience in advanced ML.

  • Experience with logistics, supply chain, or operational datasets.

  • Deep expertise in transformers, advanced forecasting methods, or unsupervised learning for structured data.

  • Publications, conference talks, or notable open-source contributions demonstrating ML innovation.

  • Experience building LLM-powered tools or applications using APIs and modern LLM frameworks.

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