Senior Machine Learning Ops Engineer

Who you’ll be joining:  

We are looking for a Senior Machine Learning Ops Engineer to join our Data Science Chapter – a team that is on a mission to make ecobee products more intelligent and personalized for our customers. We envision a future where all ecobee products work synchronously to create personalized experiences in your home.

You will be joining a team of engineers that come from diverse backgrounds and experiences in the space of ML and AI. You will work closely with Product, Data Science and Business Intelligence teams across the company on missions ranging from personalization, recommendations, energy efficiency, home security, and building a cleaner energy grid. You will also be a part of ML infrastructure development to iterate quickly, scale experiments to data sets with hundreds of billions of data points, and rapidly ship products both on the cloud and on the edge.

How you’ll make an impact:

• Build ML features on structured and unstructured content (telemetry, audio, video, user behaviour and preferences) 
• Manage the full ML development life cycle – from problem framing, data wrangling, and model development, to productionization, experimentation, and maintenance 
• Design and deploy large-scale machine learning products and solutions with correctness, usability, interpretability, experimentation, and maintainability in mind.
• Determine the feasibility of initiatives through quick prototyping with respect to performance, quality, time, and cost
• Collaborate with cross functional teams of software and data engineers to build new product features 
• Leverage your experience to drive best practices in ML Engineering and mentor other engineers on the team 
• Defining Scope and requirements, success metrics for ML projects.

What you’ll bring to the table:

• Graduate degree (Masters/PhD) or equivalent experience in Statistics, Mathematics, Computer Science or another quantitative field
• 3+ years’ experience applying machine learning to real world problems with expertise in manipulating data sets, building statistical models, and productizing machine learning solutions.
• Proven software engineering skills across multiple languages such as Python, C/C++ and ML packages
• Experience with deep learning architectures and frameworks (e.g. Pytorch, Tensorflow)
• Experience working with data at scale (1TB+), leveraging big data processing frameworks like Spark and Google Cloud Dataflo­­­­w
• 3+ years experience with software engineering and DevOps practices, MLOps deployment and infrastructure.
• Strong understanding of Scrum/Agile development technologies.
• Skilled communicator with a proven record of leading work across disciplines 
• Experience optimizing for resource constrained edge devices is a plus 
• Interest in climate change mitigation and sustainability is a plus 

We’ve built the following list as a guideline for some of the skills and interests of our development team – but we strive to build our team with members from a diverse background and skill set, so if any combination of these apply to you we’d love to chat! 

What happens after you apply:  

Application review. It will happen by an actual person in Talent Acquisition. We get upwards of 100+ applications for some roles, it can take a few days, but every applicant can expect a note regarding their application status.

Interview Process:   

• A 30-minute phone call with a member of Talent Acquisition 
• A first-round 45-minute virtual interview with the hiring manager – expect live problem-solving and interactive session, will cover technical skills and self-assessment abilities 
• The second round is a take-home assignment with an open-ended solution. This will take 2-3 hours, but you will have 48 hours to complete the assignment
• The final round will be a series of two interviews:
  • A 1-hour interview with two members of the team – expect technical, behavioral, situational, and cross-team collaboration questions. 
  • Followed by a 30-minute call with a product leader with a focus on understanding Machine Learning in a product context