Machine Learning Engineer
Skills
Cloud ComputingSoftware DevelopmentArtificial IntelligenceData ScienceEngineeringHTMLLearning Management Systems
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 5+ years of experience building and deploying machine learning systems in production environments
- Strong proficiency with model deployment technologies and inference libraries such as Kubernetes Ray ONNX vLLM TensorRT or similar
- Proficiency with model training frameworks such as PyTorch TensorFlow or Jax
- Experience designing and scaling ML services that process large volumes of data with strict latency and throughput requirements
- Experience with the full ML lifecycle including data preprocessing feature engineering model training evaluation deployment and monitoring
- Solid software engineering skills including distributed systems APIs and cloud infrastructure
- Passion for building reliable performant ML systems that create value for end users
- U.S. Person status required for access to U.S. only data systems
Nice to have
- Technical leader
- Mission-driven
- User empathy
- Disciplined growth
- Reliable
- Performant
Day to day
- Design and build scalable ML services for enrichment workflows, including model training pipelines and high-performance inference APIs
- Deploy and optimize models using modern inference libraries and frameworks to achieve low-latency high-throughput performance
- Collaborate with software engineers and product teams to define data requirements feature engineering strategies and model evaluation metrics
- Build robust monitoring observability and evaluation systems to ensure model quality and service reliability in production
- Stay current with emerging ML techniques tools and best practices especially in model optimization efficient inference and large-scale data processing