- Company Name
- tem
- Job Title
- Staff Machine Learning Engineer
- Job Description
-
**Job Title:** Staff Machine Learning Engineer
**Role Summary:**
Design, build, and scale production machine learning and quantitative models that drive pricing, matching, forecasting, and decision-making for an energy trading platform. Lead end‑to‑end ML pipelines, collaborate cross‑functionally, and mentor junior teams while maintaining high engineering and research standards.
**Expectations:**
- Deliver measurable commercial impact through advanced modeling.
- Operate in fast‑paced, ambiguous environments, translating business challenges into robust ML solutions.
- Own the full ML lifecycle from research to production, ensuring scalability, reliability, and maintainability.
**Key Responsibilities:**
- Design, implement, and operate ML and quantitative models for pricing, matching, forecasting, and decision systems.
- Translate ambiguous business problems into modeling approaches balancing accuracy, scalability, interpretability, and commercial value.
- Build cloud‑based end‑to‑end ML pipelines (data ingestion, preprocessing, training, evaluation, monitoring, retraining) on AWS.
- Reduce reliance on third‑party logic by developing proprietary modeling capabilities that evolve with product and market insights.
- Collaborate with product, engineering, and commercial stakeholders to convert business needs into technical solutions.
- Champion best practices in experimentation, reproducibility, code quality, and model governance.
- Mentor and provide technical guidance to engineers and data scientists.
**Required Skills:**
- Strong quantitative background in machine learning, optimization, or statistical modeling applied to real‑world systems.
- Proven experience building, evaluating, and shipping production‑grade ML models.
- Production engineering mindset: designing, deploying, and maintaining cloud‑based ML systems (AWS preferred).
- Expertise in Python and the modern data science/ML ecosystem (pandas, scikit‑learn, PyTorch/TensorFlow, etc.).
- First‑principles problem‑solving in ambiguous, greenfield settings.
- Commercial awareness of how modeling choices affect risk, cost, and user outcomes.
- Additional experience in time‑series, probabilistic methods, experimentation frameworks, and MLOps practices is advantageous.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Applied Mathematics, Statistics, Engineering, or a related field (or equivalent experience).
- Advanced degree (Master’s or Ph.D.) in applied mathematics, machine learning, statistics, or related disciplines is a plus.
- Certifications in cloud platforms (e.g., AWS Certified Machine Learning – Specialty) or MLOps tools are desirable.