We are seeking a skilled Machine Learning Engineer to join our growing MLOps team. In our fast-paced startup environment, you'll work closely with our data scientists and data engineers to productionize models and build robust, scalable classification systems. You will have the autonomy to drive the evolution of our ML infrastructure—from development through deployment—and make a direct impact on our product's success.

Key Responsibilities:

Develop and Optimize ML Pipelines:

Write efficient, clean, and maintainable Python code to implement machine learning pipelines for various classification projects. Ensure these pipelines meet production-grade standards for performance and scalability.

Production Deployment:

Deploy and optimize diverse classification models—including cross-encoders, bi-encoders, transformers, and custom PyTorch networks—ensuring effective GPU/CPU resource management, memory optimization, and scalability tuning.

End-to-End System Ownership:

Take full responsibility for deployed ML systems, including incident response, performance monitoring, and ongoing quality maintenance with minimal supervision.

Data Integration and Analysis:

Collaborate with the data engineering team to analyze model inputs/outputs, validate predictions, and explore potential feature improvements using SQL.

MLOps Infrastructure & CI/CD:

Build and maintain robust, extensible, and reproducible MLOps infrastructure. Establish and manage CI/CD pipelines, and set up observability for system metrics, logs, and alerts.

Collaboration and Continuous Improvement:

Contribute to technical design discussions, help break down implementation tasks, and address technical debt as needed. Work cross-functionally with both engineering and data science teams to continuously refine our deployment processes.

Day-to-Day Work

You'll be responsible for implementing and maintaining the classification systems that form the backbone of our data platform. This includes:

Translating research models into production-ready code
Developing and optimizing inference pipelines
Managing compute resources and scaling solutions
Responding to and resolving production incidents
Collaborating with data scientists to improve model deployment efficiency
Writing and analyzing SQL queries to monitor model inputs/outputs and validate prediction quality.

We're looking for someone who can work independently and take ownership while maintaining high standards. You'll have a real impact in shaping our MLOps practices and building scalable ML systems that matter. If you're passionate about turning ML research into production-ready solutions and care about writing quality code, we'd love to hear from you.

Requirements

Experience:

2+ years in machine learning engineering with a strong focus on production deployment
Proven experience deploying and optimizing transformer-based models (e.g., RoBERTa, BERT) in a production environment

Preferred Qualifications

Technical Skills:
Strong proficiency in Python and production-grade software development
Solid understanding of GPU acceleration, resource optimization, and scalable ML inference pipelines
Strong SQL skills and experience with data warehouses
Familiarity with AWS (particularly S3) and broader cloud computing concepts
DevOps & Testing:
Experience setting up observability (metrics, logging, and alerting) for ML systems
Proficiency with CI/CD practices and testing frameworks (pytest for unit, integration, and model evaluation testing)
Knowledge of version control (Git) and best practices in documentation
Problem-Solving & Communication:
Excellent problem-solving skills with a proven ability to debug complex production issues
Strong communication skills to effectively collaborate with both technical and non-technical team members
Experience with containerization (e.g., Docker) and orchestration platforms (e.g., Kubernetes)
Familiarity with serverless compute platforms (e.g., Modal) and workflow orchestration tools (e.g., Dagster)
Knowledge of additional classification models (e.g., XGBoost, FastText) and broader data engineering concepts
Understanding of data versioning, experiment tracking, and model registry practices
Experience with observability platforms (e.g., Datadog) and data warehouses like Snowflake
Experience working with LLMs in production

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Machine Learning Engineer - MLOps Team