Machine Learning Engineer

Masuda Group is seeking a Machine Learning Engineer to design, train, and deploy production ML, NLP, and LLM systems across our Experience Management (XM) platforms and data infrastructure.

In this role, you will build automated text classification, sentiment and thematic extraction, respondent fraud detection, and predictive analytics pipelines handling high-throughput survey datasets and unstructured feedback.

Technical Environment

• Languages \& Frameworks:

Python, PyTorch, Hugging Face Transformers, Scikit-learn, FastAPI

• LLMs \& Retrieval:

Open-Source LLMs, vLLM, Vector Databases (pgvector, Qdrant), LangChain, LlamaIndex

• Data \& Pipelines:

Polars, Pandas, Apache Spark, Apache Kafka

• Databases:

ClickHouse, PostgreSQL, Redis

• MLOps \& Cloud:

MLflow, Docker, Kubernetes, AWS SageMaker, GCP Vertex AI, Triton

• CI/CD \& IaC:

GitHub Actions, Terraform

Key Responsibilities

• Design, fine-tune, and evaluate machine learning and deep learning models for text classification, sentiment analysis, entity recognition, and thematic clustering.
• Develop real-time fraud detection and anomaly detection algorithms to identify bots, speeders, and low-quality responses across global panels.
• Build and scale low-latency model inference microservices and gRPC / REST endpoints using FastAPI, Triton, or vLLM.
• Deploy and maintain end-to-end MLOps pipelines covering experiment tracking, model registry, automated retraining, and data drift monitoring.
• Collaborate with backend engineers to integrate ML services into Java / Spring Boot and ClickHouse data architectures.
• Optimize model architectures for inference latency, memory footprint, and compute costs across AWS and GCP infrastructure.

Minimum Qualifications

• 3+ years of professional experience building and deploying machine learning models in production environments.
• Strong proficiency in Python and deep learning frameworks (PyTorch preferred).
• Solid experience in Natural Language Processing (NLP), transformer architectures, and embedding models.
• Hands-on experience with modern Large Language Models (LLMs), fine-tuning techniques, and vector retrieval systems.
• Experience with MLOps workflows (MLflow, Kubeflow, or cloud ML platforms) and containerization with Docker.
• Working knowledge of SQL and feature querying across PostgreSQL and ClickHouse.
• Solid understanding of probability, statistics, linear algebra, and machine learning evaluation metrics.
• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Data Science, or related quantitative field.

Preferred Qualifications

• Experience with high-throughput model serving using Triton Inference Server or vLLM.
• Experience developing fraud detection, anomaly detection, or data quality algorithms for high-volume data streams.
• Familiarity with Kubernetes orchestration and Infrastructure as Code (Terraform).

What We Offer

• Competitive compensation package.
• Flexible remote work environment with modern cloud compute infrastructure.
• Direct influence on AI, NLP, and data integrity systems used across global enterprise datasets.
• Continuous learning, conference support, and dedicated R\&D time.