Senior Data Engineer

Summary

Join Jobber's ML Platform team as a Senior Data Engineer and help machine learning engineers work 10x faster. You will build and enhance the ML platform, enabling data scientists and ML engineers to explore data, build pipelines, test models, and deploy to production. This role involves advancing the Ray-based ML platform, streamlining model deployment, enabling multi-stage environments, collaborating cross-functionally, building inference pipelines, evolving the feature store, optimizing infrastructure, and establishing best practices. You will report to the Director of Data and be part of a team shaping the foundations of Jobber's ML platform. Jobber offers a competitive salary, equity rewards, annual stipends, retirement savings matching, an extended health package, and a dedicated talent development program.

Requirements

• Strong programming skills in Python, with a background in software or data engineering
• Practical experience with data transformation, modeling, and workflow orchestration, using tools such as dbt, Airflow, or similar technologies to build modular, testable data pipelines
• Expertise in containerization and CI/CD practices, particularly with Docker and modern deployment pipelines
• Experience designing and implementing RESTful APIs that enable scalable, maintainable, and well-documented ML services
• A proven track record of platform-building, especially systems that empower stakeholders through self-serve capabilities and streamlined user experiences
• Strong grasp of DevOps principles, including infrastructure-as-code, version control, testing, and automated deployment workflows
• Excellent problem-solving abilities and attention to detail, particularly when working with complex, data-intensive systems

Responsibilities

• Advance Our ML Platform: Continue enhancing our Ray-based ML Platform by building capabilities that reduce friction across the entire ML lifecycle—from experimentation to deployment and monitoring
• Streamline Model Deployment: Design and implement tools and workflows to significantly shorten the time it takes to get models into production, ensuring both scalability and reliability
• Enable Multi-Stage Environments: Expand our platform’s environment infrastructure to support seamless development, staging, and deployment of multiple model versions with robust testing pipelines
• Collaborate Cross-Functionally: Partner with data scientists and ML engineers to gather requirements, define success criteria, and deliver on project milestones across the model development lifecycle
• Build & Maintain Inference Pipelines: Design, implement, and operate production-grade data pipelines that support model inference while meeting defined SLOs for performance and reliability
• Evolve the Feature Store: Proactively develop capabilities for our feature store to support a diverse range of ML models, including those built with deep learning frameworks
• Optimize Infrastructure: Continuously evaluate and improve the performance, reliability, and cost-efficiency of our MLOps stack
• Establish Best Practices: Define and enforce standards for version control, testing, CI/CD, and monitoring to promote reproducibility, maintainability, and trust in ML systems

Preferred Qualifications

• Hands-on experience building ML platforms or MLOps infrastructure, especially in early-stage environments
• Familiarity with Ray or other distributed computing frameworks
• Experience with caching strategies and tools to optimize data access, reduce latency, and improve performance of ML workloads
• Working knowledge of vector databases and their role in powering ML use cases such as semantic search and recommendation systems
• Experience with search technologies like Elasticsearch for efficient indexing, retrieval, and analytics
• Understanding of ML model serving frameworks (e.g., TorchServe, TensorFlow Serving) and A/B testing methodologies for evaluating model performance in production
• Contributions to open-source MLOps or ML tooling communities
• Familiarity with ML model versioning tools such as MLflow, DVC, or similar systems for tracking experiments and deployments

Benefits

• A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, retirement savings plan matching, and stock options
• A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential
• Support for all your breaks: from vacation to rest and recharge, your birthday off to celebrate, health days to support your physical and mental health, and parental leave top-ups to support your growing family