Senior ML Operations Engineer
2 weeks ago
Want to be a part of a fast moving ML team at Jobber that is supporting the business in making strategic decisions and take our product offerings to the next level?
Then Jobber might be the place for you We’re looking for a Senior Machine Learning Operations Engineer to be part of our Business Operations department.
Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber they can quote, schedule, invoice, and collect payments from their customers, while providing an easy and professional customer experience. 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. That’s why 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 by Great Place to Work, Canada’s Most Admired Corporate Cultures, and more. Jobber has also been named on the Globe and Mail’s Canada’s Top Growing Companies list, and Deloitte Canada’s Technology Fast 50, Enterprise Fast 15, and Technology Fast 500 lists. With an Executive team that has over thirty years of industry experience of leading the way, we’ve come a long way from our first customer in 2011—but we’ve just scratched the surface of what we want to accomplish for our customers .
The team:
The mandate for the ML team is to develop and deliver machine learning solutions by utilizing Jobber's internal data. The team's focus is to leverage ML and GenAI to bring predictive power into decision making for business executives, enable enhanced insights into customer behavior and build ML products that will compliment the user experience.
Like Jobber empowers small businesses with the tools and insights they need to succeed, the BizOps Department ensures our people at Jobber have the tooling, data insights, and strategic direction to excel in our shared mission. We turn data into actionable insights, and work with departments across the company to execute against their mandates. Essentially, BizOps serves as a central hub that drives business outcomes in different corners of Jobber, with specialized groups including Business Strategy, Revenue Operations, Business Analytics, and Business Technology.
The role:
Reporting to the Director, Data the Senior Machine Learning Operations Engineer will join a team that is building and enhancing Jobber's ML platform to enable Data Scientists and ML engineers build, deploy and monitor models that serve a wide variety of use-cases ranging from predictive insights, risk detection, fraud identification, product companions etc. to optimize the business process and unlock new opportunities.
We help teams leverage data, tools and technology in order to successfully execute on their own mandates. We research, develop and maintain systems which support other internal teams from an operational and analytical perspective.
We’re looking for people who are ready for their next challenge, and want to use their experience to influence people, processes and decisions.
The Senior Machine Learning Operations Engineer will:
- Setup the nextgen ML Platform at Jobber to enable Data Scientists and ML engineers to build models that provide a compelling experience across the business.
- Collaborate with Data scientists and ML/AI engineers to define the scope and understand the requirements for ML projects.
- Build pipelines to process structured/unstructured data from various sources, redact data to create PII free features and manage libraries to enable easy retrieval and combination of features.
- Responsible for complete Machine learning operations life cycle – preparing training and test data for ML engineers, data version management, assist with model tuning, deploy models to production, integrating the model with other services, monitoring model performance and setting up a closed loop for iterative model improvement.
- Drive best practices in ML Engineering, setup workshops to share knowledge within the org and mentor other engineers on the team
- Have a good understanding of LLMs and their MLOps landscape
To be successful, you should have:
- Bachelor's or Master's degree in STEM subject with a focus on software engineering or equivalent experience.
- 2+ years experience in productionizing ML models, monitor and optimize for performance
- Proven Data and software engineering skills in Python and ML packages
- Strong experience working with structured and unstructured data, leveraging big data processing frameworks like Spark
- 3+ years experience with software engineering and DevOps practices, MLOps deployment and infrastructure.
- Knowledge and experience with Terraform
- Experience working with containers, job orchestration, scripting and CI/CD.
- Strong understanding of Scrum/Agile development technologies.
- Skilled communicator with a proven record of leading work across disciplines
- Familiar with Elasticsearch, SQL, NoSQL, Apache spark, Flink, Databricks and Mlflow,
It would be really great (but not a deal-breaker) if you had:
- Understanding of deep learning architectures and frameworks (e.g. Pytorch, Tensorflow)
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