Machine Learning Software Engineer

Jobgether • United State
Remote
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AI Summary

Design and deploy machine learning systems, optimize deep learning models, and collaborate with research and domain experts to translate complex methodologies into production-grade solutions.

Key Highlights
Transform advanced machine learning models into scalable, high-performance systems
Collaborate with multidisciplinary team to build intelligent scoring and analytics solutions
Design and deploy ML infrastructure using AWS services
Key Responsibilities
Develop, optimize, and deploy end-to-end machine learning systems
Lead the transformation of machine learning models from research prototypes into scalable, production-ready systems
Build and maintain CI/CD pipelines
Technical Skills Required
Python C++ or Java AWS cloud services ECS Lambda Docker Distributed systems PyTorch ONNX conversion Quantization Pruning Flash Attention
Benefits & Perks
Competitive salary aligned with experience and technical expertise
Fully remote or flexible work arrangements
Comprehensive health, dental, and vision insurance options

Job Description


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.

Accountabilities

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

Requirements

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

Benefits

  • 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

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.


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