at Matia
Full TimeMatia is at the forefront of the DataOps revolution, building a unified platform that streamlines data management from end-to-end. We empower data teams with seamless ingestion, reverse ETL, comprehensive observability, and intuitive data cataloging, enabling faster, smarter decisions with significantly less tool sprawl. Backed by leading investors and trusted by data teams at companies like Ramp, Honeybook, and Lemonade, we're building the future of data confidence. Join us in shaping an innovative product and a high-impact engineering culture.
We are seeking an experienced and highly motivated Senior Software Engineer with a specialized focus on Machine Learning to join our innovative engineering team. In this pivotal role, you will be instrumental in bringing cutting-edge ML models into production within our DataOps platform. You’ll be responsible for the end-to-end lifecycle of machine learning solutions, from research and development to deployment and ongoing optimization, ensuring they are scalable, reliable, and integrated seamlessly into our product offerings. This is a unique opportunity to apply advanced ML techniques to complex data challenges and significantly impact Matia's future.
Design & Develop ML Systems: Design, develop, and deploy robust and scalable machine learning models and systems for our DataOps platform.
Integrate ML-Powered Features: Lead the integration of ML-powered features into existing and new services, ensuring high performance and reliability in production.
Build ML Data Pipelines: Build and optimize data pipelines specifically for ML model training, evaluation, and serving.
Implement MLOps: Drive the implementation and management of MLOps practices, including model versioning, monitoring, and automated deployment.
Cross-Functional Collaboration: Collaborate closely with product managers and other engineering teams to translate business problems into effective ML solutions.
Research & Innovate: Conduct research and experimentation with new ML techniques and frameworks to drive innovation within our platform.
Troubleshoot & Optimize: Troubleshoot and resolve complex technical issues related to ML systems in both development and production environments.
We are seeking a hands-on ML practitioner who enjoys bringing advanced machine learning models from concept to production, capable of driving ML initiatives and ensuring models are robust, scalable, and deliver real business value.
Must-Haves:
Experience: 5+ years of hands-on experience in software development, with a strong focus on machine learning.
Production ML Expertise: Proven experience in designing, building, and deploying scalable ML models and systems in production environments.
Programming & Frameworks: Exceptional proficiency in Python and popular ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers.
MLOps Mastery: Strong understanding and practical application of MLOps principles and tools (e.g., MLflow, Kubeflow, SageMaker, Airflow).
Data Pipelining: Experience with data processing and pipeline tools (e.g., Spark, Kafka) for ML workloads.
Cloud Infrastructure (AWS): Strong understanding of system architecture and cloud platforms, especially AWS, including services relevant to ML (e.g., SageMaker, Lambda, EC2).
Software Engineering Fundamentals: Solid software engineering fundamentals for building robust, maintainable ML systems.
Independent & Collaborative: Ability to work independently, taking complex features or projects from conception to completion with minimal oversight, while also possessing excellent communication and collaboration skills to work effectively across disciplines.
Nice-to-Haves:
Experience designing and implementing time-series-based anomaly detection algorithms at scale.
Specialized Databases: Experience with vector databases (e.g., Pinecone, Weaviate, FAISS).
Data Tools Domain: Experience within a data tools company (e.g., ETL, Reverse ETL, Observability, Catalog).
Mindset: A curious problem-solver passionate about applying ML to complex data challenges within a product context; hungry to build.
Advanced ML/AI: Experience with Large Language Models (LLMs), agent architectures, or prompt engineering.
