Machine Learning Engineer

Neptune Technologies Inc.; United States; Remote; USD 100,000 - 720,000; Full time

Job summary

Neptune Technologies Inc. is seeking a Machine Learning Engineer to build production-ready ML systems and implement MLOps best practices. In this full-time remote role, you will design, train, and deploy machine learning models, ensuring their performance in production environments. The ideal candidate will have a Master's or Ph.D. in a relevant field and extensive experience with ML frameworks and MLOps tools.

Benefits

Competitive salary + benefits

Equity participation in a fast-growing AI startup

Remote-first culture with flexible working hours

Qualifications

• 4+ years of hands-on experience building and deploying ML models in production environments.
• Strong proficiency in Python and ML frameworks like TensorFlow and PyTorch.
• Experience with MLOps tools and practices.

Responsibilities

• Design, develop, and deploy scalable machine learning models and pipelines.
• Implement end-to-end MLOps workflows including model training and monitoring.
• Optimize models for production deployment including quantization and pruning.

Skills

Python

Machine Learning frameworks

Deep Learning architectures

Statistical analysis

NLP techniques

Education

Master's or Ph.D. in Computer Science, Machine Learning, or related field

Tools

MLflow

Kubeflow

AWS SageMaker

Apache Spark

Docker

Job description

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Company DescriptionNeptune Technologies Inc. transforms business operations through agentic artificial intelligence, enabling organizations to automate complex workflows with intelligent AI systems. Neptune empowers legal, financial, and healthcare enterprises to deploy AI "employees" using their proprietary data and systems 10-100x faster than traditional approaches. Leading professional services firms, financial institutions, and healthcare organizations rely on Neptune's solutions. Founded in 2023, Neptune's technology combines cutting-edge agentic frameworks with enterprise-grade security, ensuring seamless integration with existing applications while maintaining complete data protection and compliance.

Role DescriptionThis is a full-time remote role for a Machine Learning Engineer focused on building production-ready ML systems and implementing MLOps best practices. You will be responsible for designing, training, and deploying machine learning models at scale, building robust ML pipelines, and ensuring model performance in production environments. The role involves working with large-scale data infrastructure, implementing automated ML workflows, and maintaining model versioning and monitoring systems. You'll collaborate closely with data engineers, software engineers, and product teams to integrate ML solutions into our agentic AI platform.

Key Responsibilities:

• Design, develop, and deploy scalable machine learning models and pipelines for agentic AI applications
• Implement end-to-end MLOps workflows including model training, validation, deployment, and monitoring using tools like MLflow, Kubeflow, or AWS SageMaker
• Build and maintain feature engineering pipelines and feature stores for real-time and batch processing
• Develop automated model retraining pipelines with drift detection and performance monitoring
• Optimize models for production deployment including quantization, pruning, and edge deployment when necessary
• Implement A/B testing frameworks for model performance evaluation in production
• Design and maintain model APIs and microservices using FastAPI, Flask, or similar frameworks
• Ensure model explainability, fairness, and compliance with regulatory requirements in financial and healthcare domains
• Build data pipelines for training data preparation using Apache Spark, Airflow, or similar tools
• Implement model versioning, experiment tracking, and reproducibility standards

Qualifications

• Master's or Ph.D. in Computer Science, Machine Learning, or related field (or equivalent experience)
• 4+ years of hands-on experience building and deploying ML models in production environments
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
• Extensive experience with MLOps tools and practices:
• Experiment tracking and model registry (MLflow, Weights & Biases)
• CI/CD for ML pipelines (GitHub Actions, GitLab CI)
• Model monitoring and observability (Evidently AI, Whylabs, or custom solutions)
• Strong understanding of:
• Deep learning architectures (transformers, CNNs, RNNs, attention mechanisms)
• NLP techniques for agentic AI applications
• Statistical analysis and experimental design
• Distributed computing and parallel processing
• Experience with cloud platforms and their ML services:
• AWS (S3, SageMaker, Lambda, Batch, EMR)
• Proficiency in containerization (Docker) and orchestration (Kubernetes)
• Experience with vector databases (Pinecone, Weaviate, Milvus) for embedding-based applications

Additional Preferred Skills:

• Experience with LLMs and prompt engineering for agentic AI systems
• Knowledge of reinforcement learning for autonomous agent development
• Familiarity with financial markets, legal tech, or healthcare domains
• Experience with real-time ML serving and edge deployment
• Understanding of privacy-preserving ML techniques (federated learning, differential privacy)
• Contributions to open-source ML projects
• Experience with streaming data processing (Kafka, Kinesis)
• Knowledge of model compression techniques for efficient deployment

Technical Stack:

• ML Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, LangChain
• Data Tools: Apache Spark, Airflow, dbt, Great Expectations
• Monitoring: Prometheus, Grafana, CloudWatch, custom ML monitoring solutions

Compensation

• Competitive salary + benefits (stock options, health insurance and more)
• Equity participation in a fast-growing AI startup
• Remote-first culture with flexible working hours

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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