Staff Engineer, Machine Learning

Who You’ll be Joining: We are seeking a Staff Machine Learning Engineer to join our Data Science Chapter, a team dedicated to enhancing the intelligence and responsiveness of our product ecosystem to meet our customer's unique needs. You will report directly to the VP of Data Science and will support various teams within the Data Science Chapter, composed of engineers with diverse backgrounds in machine learning and artificial intelligence. How You’ll Make an Impact: Working with multiple teams within Data Science, you will use your machine learning expertise to aid and progress different projects while also closely collaborating with members of our Product and Business Intelligence teams on tasks like personalization, recommendations, energy efficiency, home security, and developing a cleaner energy grid. You will directly contribute to the establishment of our machine learning infrastructure, quickly iterating, conducting and scaling tests with datasets containing hundreds of billions of data points, and rapidly launching products on both the cloud and the edge. As a Staff Machine Learning Engineer at ecobee, you will: - Lead the development of advanced machine learning algorithms to personalize the customer journey across various touch points, including paid ads, SEO content, web pages, and in-product experiences. - Collaborate closely with technical and non-technical stakeholders, including business partners, along with engineering and analytics teams. - Manage the completemachine learning development lifecycle from problem framing, data wrangling, and model development to productionization, experimentation, and maintenance. - Design and deploy large-scale machine learning products on structured and unstructured content (telemetry, audio, video, user behavior, and preferences), focusing on correctness, usability, interpretability, experimentation, and maintainability. - Assess the feasibility of initiatives through fast prototyping and against requirements of performance, quality, time, and cost. - Use your experience to advance machine Learning Engineering best practices and mentor Senior Engineers within the Data Science Chapter. - Incorporate cutting-edge research and industry knowledge into the team around natural language processing (NLP), computer vision, machine learning, generative AI, and related areas. What You’ll Bring to the Table We have established the following list as a guideline for some of the skills and interests our development team should have. However, we strive to build our team with members from diverse backgrounds and skill sets. So if you have any combination of these, we'd love to talk! - A graduate degree (Masters/PhD) in Statistics, Mathematics, Computer Science, or another quantitative field. - Extensive experience applying machine learning to multiple projects aimed at solving real-world problems. - Substantial experience with data manipulation, building statistical models, and productionizing machine learning solutions using state-of-the-art and big data technology. - Experience working with data on the scale of 1TB or more, leveraging big data processing frameworks like Spark and Google Cloud Dataflow. - Experience with software engineering and DevOps practices and ML-Ops deployment and infrastructure. - Experience optimizing for resource-constrained edge devices. - Proven software engineering skills in multiple languages such as Python, C/C++, and associated frameworks and libraries used in machine learning packages. - Demonstrated ability to lead cross-functional projects to successful conclusions using strong problem-solving skills and the ability to communicate complex concepts to both technical and non-technical stakeholders. - Proven ability to develop and train production-ready machine learning models including transformer models, reinforcement learning, neural networks, model training in areas like classifiers, recommenders, vision, and speech. - Expertise with implementing details of large neural-network architectures using frameworks like PyTorch, Tensorflow, Keras, JAX, and so on. - Experience working in environments that use Scrum and Agile. - Knowledge of how to manipulate data for analysis, including querying data, defining metrics, and slicing data to evaluate hypotheses. What happens after you apply? Application Review: A genuine person in Talent Acquisition will review it. We typically receive upwards of 100+ applications for some roles. It may take a few days, but every applicant will receive a status update on their application. Interview Process (3 Rounds): - Round 1: A 45-minute phone call with a member of Talent Acquisition. - Round 2: A 45-minute virtual meeting with a Senior Manager in Data Science. This will be a technical interview structured as a Q&A session. - Round 3 is divided into two parts: Part 1: A 1-hour virtual meeting with a pair of senior members of our Machine Learning team. You’ll present your solution to a case study (provided in advance for preparation) and answer questions about your presentation. Part 2: A 45-minute virtual meeting with our VP of Data Science and a Staff Product Manager for a Q&A session on machine learning in a product context.