Senior Machine Learning Operations Engineer, Jobber
MLOps
+ Stock options
Job tracking & customer management
Be an early applicant
Job tracking & customer management
501-1000 employees
Be an early applicant
+ Stock options
501-1000 employees
Company mission
To help small business owners move more efficiently, survive changes in the economy, support their families and communities, and WIN at creating something on their own.
Role
Who you are
- We’re looking for people who are ready for their next challenge, and want to use their experience to influence people, processes and decisions
- 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
Desirable
- Demonstrated experience in building ML platforms or MLOps infrastructure
- Experience with Polars, a high-performance DataFrame library for Rust and Python
- Familiarity with caching tools and strategies for optimizing data access and processing
- Knowledge of vector databases and their applications in machine learning pipelines
- Experience with search engines like Elasticsearch for efficient data indexing and retrieval
- Understanding of ML model serving frameworks and A/B testing methodologies
- Contributions to open-source MLOps tools or frameworks
- Familiarity with ML model versioning tools (e.g., MLflow, DVC)
What the job involves
- Reporting to the Director, Data the Senior Machine Learning Operations Engineer on the MLOps team will be building an ML platform from the ground up to unlock improved operational outcomes, workflow efficiencies and new business insights across our organization
- We help teams at Jobber leverage data, tools and technology to successfully execute on their mandates
- We research, develop and maintain systems which support other internal teams from an operational and analytical perspective
- Collaborate in architecting and building a comprehensive ML Platform from the ground up, enabling Data Scientists and ML engineers to efficiently develop, deploy, and manage ML models
- Lead collaboration efforts with Data Scientists and ML engineers to define the scope, requirements, and success criteria for ML projects, ensuring alignment with business objectives
- Design and implement robust data pipelines to process raw structured and unstructured data, proactively building features for feature stores to support diverse ML use cases
- Oversee the complete MLOps lifecycle, including requirements gathering, data cleaning and organization, model development, production deployment, monitoring, and maintenance
- Conduct thorough feasibility analyses through proofs-of-concept (POCs) and provide data-driven recommendations on preferred approaches, tools, and products within the open-source MLOps ecosystem
- Develop and maintain a deep understanding of Large Language Models (LLMs) and their specific MLOps requirements, staying current with rapid advancements in this field
- Implement and optimize end-to-end MLOps pipelines for model training, evaluation, and deployment, ensuring scalability and efficiency
- Establish and implement best practices for version control, testing, and monitoring of ML models, promoting reproducibility and reliability
- Architect scalable and efficient data processing systems capable of handling large-scale machine learning applications
- Continuously assess and improve the MLOps infrastructure to enhance performance, reliability, and cost-effectiveness
Our take
For businesses that sell home services, it can be difficult keeping up with paperwork, scheduling appointments, and chasing payments, especially when their teams are small. Jobber delivers a comprehensive toolbox to keep track of all these aspects, so that small business owners can concentrate on more important things like serving their customers and building their brand.
Jobber was founded in 2011, initially to help a friend who owned a small painting business. The founders quickly discovered that what they were building could help many more owners across a number of industries, going on to launch the product which is now used by over 200,000 professionals.
What makes Jobber stand out is that it collects all the vital parts of running a home services business into one place, removing the burden of keeping track of multiple sources of information. The company has seen significant funding which it has invested in R&D and growing its customer base, and boasts four consecutive years on The Globe and Mail's Canada's Top Growing Companies List.
Steph
Company Specialist
Insights
Company
Funding (last 2 of 7 rounds)
Jan 2023
$100m
SERIES D
Jan 2021
$60m
GROWTH EQUITY VC
Total funding: $185.8m
Company benefits
- Health, dental, vision, and paramedical for both mind and body, life and travel insurance, and an employee assistance program.
- Health spending and wellness accounts to help with expenses not covered by traditional benefits.
- Equity and RRSP matching of up to 3% of your annual salary.
- Your birthday off!
- Parental leave—complete with top-ups for up to 8 weeks.
- Monthly snack box program with plenty of options for that afternoon pick-me-up.
- Bi-weekly all company stand-ups, quarterly hackathons and town halls, and yearly all-hands professional development sessions.
- Continuous 1:1’s and honest feedback.
- A team of humble and supportive group of Jobberinos who give a sh*t about the work they’re doing.
- Opportunity to have a 1:1 session with one of our Development Coaches, take advantage of our in-house suite of learning opportunities, and build out your personal development plans.
- Hybrid work model.
- Work in either our Edmonton or Toronto office, remotely from anywhere in Canada or the US, or a combination of both.
- Monthly home office allowance and a one-time stipend to help equip your home office.
Company HQ
Downtown, Edmonton, AB
Articles
Leadership
Sam Pillar
(CEO)Former Freelance Software Developer. Previously a Business Analyst and Management Consultant at CGI.
Forrest Zeisler
(CTO)Experienced Developer, with a degree in Engineering from the University of Alberta.
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