Senior MLOps Engineer

September 26

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Jobber

WebsiteLinkedInAll Job Openings

Cloud Based Business Management Software • Field Service Business Management • Mobile Solutions • SaaS • CRM

501 - 1000

Description

•Jobber exists to help people in small businesses be successful. •We work with small home service businesses to transform the way service is delivered through technology. •With Jobber they can quote, schedule, invoice, and collect payments from their customers. •Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. •We put the power and flexibility in their hands to run their businesses how, where, and when they want! •Our culture of transparency, inclusivity, collaboration, and innovation has been recognized. •The Senior Machine Learning Operations Engineer will be building an ML platform from the ground up to unlock improved operational outcomes and workflow efficiencies across our organization.

Requirements

•A background in software or data engineering •Polished communication skills with a proven record of leading work across disciplines •Strong proficiency in Python programming •Extensive experience with Apache Spark for large-scale data processing •Expertise in containerization, particularly Docker and CI/CD technologies •Experience designing and implementing RESTful APIs •Comprehensive knowledge of AWS services, including: ECS Fargate for container orchestration, EMR (Elastic MapReduce) for big data processing and AWS Glue for ETL workflows •Proven track record of building and maintaining complex ETL pipelines •Experience with workflow management tools, specifically Apache Airflow •Proficiency in using dbt (data build tool) for data transformation and modelling •Strong understanding of DevOps principles and CI/CD practices •Excellent problem-solving skills and attention to detail •Ability to work effectively in a fast-paced, collaborative environment •Demonstrated experience in building ML platforms or MLOps infrastructure (great, but not a deal-breaker) •Experience with Polars, a high-performance DataFrame library for Rust and Python (great, but not a deal-breaker) •Familiarity with caching tools and strategies for optimizing data access and processing (great, but not a deal-breaker) •Knowledge of vector databases and their applications in machine learning pipelines (great, but not a deal-breaker) •Experience with search engines like Elasticsearch for efficient data indexing and retrieval (great, but not a deal-breaker) •Understanding of ML model serving frameworks and A/B testing methodologies (great, but not a deal-breaker) •Contributions to open-source MLOps tools or frameworks (great, but not a deal-breaker) •Familiarity with ML model versioning tools (e.g., MLflow, DVC) (great, but not a deal-breaker)

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, •Stock options. •A dedicated Talent Development function, including Development Coaches, to help build the career you want and hit the goals you set, while ensuring you’re reaching your fullest 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. •A unique opportunity to build, grow, and leave your impact on a $400-billion industry that has no dominant player...yet. •To work with a group of people who are humble, supportive, and give a sh*t about our customers.