Senior Software Engineer, Machine Learning

Who you’ll be joining:  

We are looking for a Senior Software 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 who 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 be a part of ML platform development to rapidly design, secure, build, test and release model and feature serving at scale and to ship products both on the cloud and on the edge. The role is primarily for an individual contributor who will be a key notable contributor to the MLOps platform at ecobee and collaborate closely with the internal cross-functional development teams.

How you’ll make an impact:

• Collaborate with a team of machine learning engineers and data scientists to develop robust machine learning model pipelines, architect and implement APIs, and create microservices focused on optimizing latency, availability, and overall performance. 
• Implement best practices for version control, code review, testing, and documentation, fostering a culture of high-quality software development
• Stay current with the latest tools, technologies, and best practices in machine learning engineering and cloud-based infrastructure, and drive continuous improvement within the team
• Monitor, troubleshoot, and optimize the performance of machine learning models and related infrastructure
• Leverage your experience to drive best practices in ML Engineering and mentor other engineers on the team 

What you’ll bring to the table:

• Graduate degree (Masters/PhD) or equivalent experience in Computer Science or another quantitative field
• 5+ years’ experience productizing software solutions to real world problems, focussed on machine learning is a plus.
• Proven software engineering skills across multiple languages and GCP tooling (BigQuery, Vertex AI, composer, etc..)
• 3+ years experience with software engineering and DevOps practices, MLOps deployment, workflow orchestration and infrastructure.
• Experience working with data at scale (1TB+), leveraging big data processing frameworks like Spark and Google Cloud Dataflo­­­­w
• Strong understanding of Scrum/Agile development technologies.
• Skilled communicator with a proven record of delivering 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 1-hour virtual interview with the hiring manager and SWE manager – expect live problem-solving and interactive session, will cover technical skills and self-assessment abilities 
• 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 leader with a focus on understanding Machine Learning in a product context.