Senior Machine Learning Engineer, ecobee
Smart & sustainable home appliances
Be an early applicant
Smart & sustainable home appliances
501-1000 employees
Be an early applicant
501-1000 employees
Company mission
To improve everyday life, while creating a more sustainable world.
Role
Who you are
- 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
What the job involves
- We are looking for a Senior Machine Learning 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
- 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
Application process
- Just so you know: The hired candidate will be required to complete a background check
- 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
- 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 will be a 3 part technical interview for 90 minutes:
- Part 1: Leet Code style coding exercise (20 minutes)
- Part 2: ML System Design (40 minutes)
- Part 3: Technical deep dive discussion (30 minutes)
- The final round will be a 45-minute call with a leader with a focus on evaluating cultural and team fit
Our take
As the world shifts toward sustainable transport, housing, and lifestyle solutions, ecobee's smart home devices are well-positioned to address domestic energy efficiency. Known for creating the world's first WiFi-enabled thermostat, ecobee offers a range of smart home products.
Despite being an early market entrant, ecobee has expanded its product line to stay competitive. By integrating Alexa into its devices, ecobee has significantly grown its customer base and strengthened its relationship with Amazon, similar to how its competitor, Nest, partnered with Google.
In 2021, ecobee was acquired by Generac, a major power industry player aiming to expand into sustainable solutions. This acquisition has provided ecobee with the resources to reach a global market and continue leading in sustainability innovations.
Freddie
Company Specialist at Welcome to the Jungle
Insights
7% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
May 2018
$36m
SERIES C
Mar 2018
$80m
SERIES C
Total funding: $167.8m
Company benefits
- Health/Lifestyle Spending Account
- 75% parental leave top-up
- Health benefits starting day 1
- 25-day paid sabbatical in year 5
- $1,500 learning and development budget
- Quarterly hackathons
- Volunteer Days
- Flexible hours and work from home
- Your own ecobee device
Company values
- Cultivate curiosity
- Start with the customer
- Aim for impact
- Work for the planet
- Think like an owner
- Give respect and build trust
Company HQ
Central Waterfront, Toronto, ON
Articles
Leadership
Stuart Lombard
(CEO)Previously Founder of Inforamp. Has also served as Co-CEO at Isolation Systems and VP of R&D at Grocery Gateway.
Mark Malchionodo
(VP of Software Engineering)Previously CTO at PlanetEye Inc, Engineering Manager at BorderWare Technologies, Engineering Manager at Intel of Canada and Software Architect at Software Group Limited.
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