Machine Learning Engineer
# Machine Learning Engineer **Swish Analytics** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Mid-level` 💰 **Компенсация:** `$160,000 – $160,000` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineering]` `[Data-Science]` `[Software-Engineer]` `[Sports-Analytics]` `[Quantitative-Analytics]` `[Machine-Learning-Engineer]` `[Machine-Learning-Engineer-Jobs]` `[AI-ML-Engineer]` `[Machine-Learning-Engineering-Jobs]` `[Applied-Machine-Learning-Engineer]` --- ### About the Role Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients. The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to “roll your own” and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues. This position is 100% remote Responsibilities: - Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency. - Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow. - Build, test, deploy and maintain production systems. - Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages. - Support maintenance and optimization of cloud-native EDW and ETL solutions. - Maintain and promote best practices for software development, including deployment process, documentation, and coding standards. - Experience applying large scale data processing techniques to develop scalable and innovative sports betting products. - Use extensive experience to build, test, debug, and deploy production-grade components. - Experience applying large scale data processing techniques to develop scalable and innovative sports betting products. - Participate in development of database structures that fit into the overall architecture of Swish systems Qualifications: - Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area - 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs - A proven background in quantitative analytics, trading, or engineering is required for this position - Demonstrated experience developing data science modeling systems and infrastructure at scale - Experience with Python and exposure to modern machine learning frameworks - Proficient in SQL; experience with MySQL - Background and/or interest in Rust preferred - Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback - Strong communication skills when discussing technical concepts with technical and non-technical colleagues Base salary: starting at $160,000 base plus bonus potential Originally posted on Himalayas
- Machine-Learning-Engineering
- Data-Science
- Software-Engineer
- Sports-Analytics
- Quantitative-Analytics
- Machine-Learning-Engineer
- Machine-Learning-Engineer-Jobs
- AI-ML-Engineer
- Machine-Learning-Engineering-Jobs
- Applied-Machine-Learning-Engineer
Наблюдалась 2026-10-02, впервые 2026-10-02, источник — Himalayas (JSON API).