Senior MLOps, Data Systems Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Development of ML Pipelines & Data Systems: Design, construct, and sustain scalable pipelines that encompass data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability throughout the entire ML lifecycle.

• Integration of Data & Annotation Pipelines: Build and merge annotation workflows with upstream data ingestion and training systems, facilitating efficient task creation, labeling, quality assurance, and dataset updates that directly enhance model iteration.

• Data-Centric Iteration: Evaluate model performance and failures, and drive focused data enhancements by linking production signals, data mining, and annotation workflows into continuous feedback loops.

• Experimentation & Reproducibility: Establish systems for tracking experiments, versioning datasets, and managing model lineage to support reliable comparisons and iterations across experiments.

• CI/CD for Machine Learning: Create and uphold CI/CD workflows customized for ML systems, allowing for automated testing, validation, and deployment of models and pipelines.

• Support for Model Deployment: Collaborate with embedded and platform teams to facilitate the deployment of models in edge environments, ensuring compatibility, performance, and reliability.

• Monitoring & Feedback Mechanisms: Implement systems for monitoring, logging, and feedback to track model performance in production and promote continuous improvement through data and model iterations.

• Compute Optimization: Enhance training and inference workflows across cloud environments, ensuring efficient use of GPU and compute resources.

• Cross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure coherence across data workflows, model development, and deployment systems.

• End-to-End Contribution: Engage in and enhance the full ML lifecycle, from raw data ingestion and annotation to training, evaluation, deployment support, and post-deployment analysis.

⛳️ Requirements

• More than 5 years of professional experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or similar positions.

• Proficient programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.

• Proven experience in constructing and maintaining comprehensive ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.

• Experience in designing or integrating annotation and data curation workflows, along with an understanding of how labeled data influences model performance.

• Solid grasp of dataset versioning, data lineage, and reproducibility within machine learning systems.

• Familiarity with experiment tracking and management of the model lifecycle.

• Knowledge of CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and their application to machine learning workflows.

• Experience with containerization (Docker) and workflow orchestration systems.

• Background in cloud-based ML environments (e.g., AWS) and distributed training workflows.

• Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across different environments.

• Excellent problem-solving and debugging abilities, especially in complex, multi-stage systems.

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).

🏝️ Benefits

• Comprehensive Health & Wellness: A selection of medical, dental, and vision plans, along with company-paid life and disability insurance and company-funded mental health benefits.

• Financial & Retirement Planning: Access to a 401(k) plan with both pre-tax and Roth options, plus a Health Savings Account (HSA) with a monthly company contribution.

• Family & Fertility Support: Paid parental leave for both birthing and non-birthing parents, as well as benefits for fertility and family formation.

• Paid Time Off: Unlimited vacation days, paid leaves, and 10 company-observed holidays.

• Unique Lime Perks: Complimentary access to Lime vehicles in participating cities, a monthly phone stipend, dedicated learning and development days, and additional perks including One Medical, Wellhub, and Headspace.