Machine Learning Engineer

As a Machine Learning Engineer, you will be a key contributor to building the models and pipelines that power our industrial contextualization platform. Working closely with senior and staff-level engineers, you will focus on execution, transforming research prototypes into high-performance, real-time applications and building the infrastructure that supports them. This is an engineering-first role. We seek hands-on builders who write production-grade code, understand system design, and tackle complex infrastructure, treating ML models as software components in a large industrial architecture.

Responsibilities

Build, test, and maintain robust data pipelines for large-scale industrial datasets; Assist in deploying data science prototypes to production; Write high-quality, testable Python code, primarily creating RESTful/gRPC APIs for ML capabilities; Containerize applications (Docker) and navigate Linux to handle massive volumes of unstructured industrial data; Run experiments, fine-tune existing foundational models (NLP, Vision-Language), and evaluate ML libraries to enhance document parsing and entity matching; Write rigorous unit/integration tests and monitor deployed models for drift and performance; Collaborate closely with senior staff (engineers, data scientists, product managers) to deliver features on the technical roadmap

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field; 3–6 years of industry experience in software engineering with a strong focus on machine learning, or vice versa; Strong programming skills in Python, with a proven ability to write clean, modular, and testable code; Solid hands-on experience with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain; Experience working with large datasets, writing complex SQL queries, and hands-on familiarity with data processing libraries or frameworks (e.g., PySpark, Pandas at scale, or Dask); Familiarity with MLOps basics, containerization (Docker), Linux command-line tools, and fundamental cloud services (AWS, Azure, or GCP); Solid understanding of core computer science fundamentals, data structures, and algorithms

Nice to have

Academic or early industry experience with Vision-Language Models (VLMs) or Computer Vision (e.g., document layout analysis); Hands-on familiarity with basic RAG pipelines and Vector Databases (e.g., Pinecone, Milvus, Qdrant); Exposure to agentic frameworks (e.g., LangGraph); Exposure to modern columnar data (e.g., Parquet) or data lakes (e.g., Delta Lake, Apache Iceberg); Experience with highly unstructured data, including text and JSON extraction from messy PDFs using OCR; OT dataset exposure; Experience serving models via backend web frameworks (FastAPI/Flask); Comfortable troubleshooting Linux permissions, fonts, and dependencies