Senior Machine Learning Engineer
Actively Hiring
Full-time Posted 6 months ago
Responsibilities
- check_circle Collaborate with data scientists, product engineers, and stakeholders to design, implement, and technically lead ML‑powered solutions
- check_circle Build and maintain reusable libraries, pipelines, and platform tools that enable efficient model training, deployment, and monitoring
- check_circle Contribute to evolving our domain models and ontology layer that supports consistent data and ML integration
- check_circle Iterate with users to ensure solutions are intuitive, performant, and aligned with their workflows
- check_circle Apply software engineering best practices, including secure coding, modular design, and CI/CD automation
- check_circle Write comprehensive unit and performance tests to ensure system stability and prevent regression
- check_circle Proactively identify and resolve technical debt, performance bottlenecks, and scalability challenges
- check_circle Document systems and code for maintainability and team‑wide knowledge sharing
- check_circle Stay current with industry best practices in MLOps, cloud computing, and ML system design
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 5+ years of experience in software engineering or machine learning engineering
- Experience in the logistics/supply chain industry or with a company where building software solutions is a core part of the business (e.g., product‑driven environments, technology‑led organizations, or companies with strong supply‑chain or data‑heavy operations)
- Proficiency in Python and C#/.NET Core for backend and ML‑integrated workflows
- Strong experience with Azure ML, Azure Cloud Services, and containerized environments (Kubernetes)
- Experience designing and building scalable data or ML pipelines using tools like Airflow/Astronomer
- Familiarity with data platforms such as Snowflake, PostgreSQL, MongoDB, and Redis
- Solid understanding of distributed systems, microservices, and message‑based architectures (e.g., Kafka)
- Proven experience writing production‑level code with attention to testing, logging, and monitoring
Preferred qualifications
- Demonstrated experience leading end‑to‑end delivery of production ML systems or platform components
- Experience implementing and scaling MLOps practices in a production environment
- Hands‑on experience working with large datasets and distributed training workflows
- Familiarity with feature stores, model registries, and experiment tracking tools
- Contributions to internal or open‑source ML libraries/platforms
- Ability to mentor junior engineers and influence engineering best practices
Benefits
- check_circle Real opportunities to grow your talent in a fast-moving, global organization
- check_circle A fun, open, and inclusive workplace that encourages innovative thinking
- check_circle Possibility to develop your language skills in our multilingual offices
- check_circle Opportunities for professional growth with access to training platforms like
- check_circle Comprehensive benefits: Private medical insurance, additional Life insurance, Employee Assistance Program (EAP), Employee Stock Purchase Plan (ESPP) after one year of employment
Tags & Focus Areas
Fulltime Machine Learning Data Science Ai