Machine Learning Software Engineer in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Software Engineer in United States.

This role sits at the intersection of applied research and production engineering, transforming advanced machine learning models into scalable, high-performance systems. You will collaborate with a multidisciplinary team of mathematicians, statisticians, psychometricians, and computer scientists to build intelligent scoring and analytics solutions. The position involves taking ownership of the full ML lifecycle, from prototype to production, with a strong focus on cloud-native deployment and system optimization. You will design and implement robust architectures that support high-throughput, low-latency inference in real-world applications. The environment is highly collaborative and research-driven, encouraging innovation and technical depth. This is an opportunity to shape impactful ML systems that directly influence educational and analytical outcomes at scale.

In this role, you will be responsible for developing, optimizing, and deploying end-to-end machine learning systems that bridge research and production environments.

• Lead the transformation of machine learning models from research prototypes into scalable, production-ready systems
• Design and deploy ML infrastructure using AWS services such as ECS for containerized workloads and Lambda for serverless inference
• Optimize deep learning models (primarily PyTorch) through ONNX conversion and advanced techniques such as quantization, pruning, and Flash Attention
• Build and maintain CI/CD pipelines ensuring reliable, secure, and reproducible ML deployments across cloud environments
• Develop algorithms producing descriptive, diagnostic, predictive, and prescriptive insights from structured and unstructured data
• Write high-quality, testable, and well-documented code while ensuring system reliability through debugging and performance tuning
• Collaborate closely with research and domain experts to translate complex methodologies into production-grade solutions

The ideal candidate combines strong machine learning expertise with solid software engineering and cloud infrastructure experience.

• 2–5 years of experience in Machine Learning Engineering, Software Engineering, or Data Science with production deployment experience
• Strong proficiency in Python and familiarity with C++ or Java, along with strong software engineering practices
• Hands-on experience with AWS cloud services, especially ECS and Lambda, and solid understanding of Docker and distributed systems
• Strong experience with ML frameworks such as PyTorch (and familiarity with TensorFlow and Scikit-learn)
• Deep understanding of model optimization techniques, including ONNX conversion, inference acceleration, and memory-efficient architectures
• Experience working with large-scale data systems, including both relational and non-relational databases
• Strong analytical mindset, problem-solving ability, and effective communication skills for cross-functional collaboration
• Nice to have: experience with AWS SageMaker, LLM fine-tuning techniques (LoRA, qLoRA), AI agents, NLP systems, Infrastructure as Code (Terraform or CloudFormation), and model monitoring in production

• Competitive salary aligned with experience and technical expertise
• Fully remote or flexible work arrangements depending on role setup
• Comprehensive health, dental, and vision insurance options
• Opportunity to work on high-impact ML systems used in real-world educational applications
• Access to a highly collaborative, research-driven, and interdisciplinary environment
• Strong focus on professional growth, learning, and technical development
• Inclusive workplace culture supporting diversity, equity, and accessibility