Senior Machine Learning Engineer at Tantalus
Burnaby, BC V5G 0B3, Canada -
Full Time


Start Date

Immediate

Expiry Date

05 May, 25

Salary

0.0

Posted On

06 Feb, 25

Experience

10 year(s) or above

Remote Job

No

Telecommute

No

Sponsor Visa

No

Skills

Computer Science, Risk Analysis, Mathematics, Project Teams, Testing, Fine Tuning, Integration, Large Scale Systems, Data Analysis, Java, Encoders, Distribution Systems, Python, Deep Learning, Data Extraction, Programming Languages, Sql, C++, Infrastructure, Data Processing

Industry

Information Technology/IT

Description

ABOUT TANTALUS SYSTEMS (TSX: GRID)

Tantalus is a technology company dedicated to helping utilities modernize their distribution grids by harnessing the power of data across all their devices and systems deployed throughout the entire distribution grid – from the substation to the EV charger located behind the meter. We offer smart grid solutions across multiple levels: intelligent connected devices, communications networks, data management, enterprise applications and analytics.
Learn more at http://www.tantalus.com/.
Come join us if you’re interested in being part of an entrepreneurial team, solving complex technical problems and delivering innovative solutions that will directly impact the electrification of everything and the decarbonization of our society.
We have operations throughout the United States and Canada with offices in Burnaby (British Columbia, Canada), Raleigh (North Carolina, USA), and Norwalk (Connecticut, USA).
This position offers a competitive salary plus variable compensation based on performance targets and business objectives. Tantalus also offers generous benefits, including medical, dental and vision plans, healthcare and dependent care flexible spending accounts and paid time off.

ESSENTIAL QUALIFICATIONS:

  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
  • 7-10+ years of experience delivering large-scale systems to production environments.
  • A keen interest in time-series ML problems, Edge-ML and distributed systems.
  • Demonstrated experience in the following:
  • Building machine learning solutions leveraging architectures such as deep learning (e.g., LSTM, CNNs), reinforcement learning, and transformers.
  • Architecting and developing software or infrastructure for scalable, distributed systems.
  • Leading technical projects in ML design and optimizing large-scale ML infrastructure (e.g., model deployment, evaluation, data preprocessing, fine-tuning).
  • Designing cloud-native solutions and infrastructure for real-time or batch data processing.
  • Software design and architecture, including testing and launching products into production.
  • Strong experience across a broad range of programming languages including but not limited to Python, Java and C++.
  • Strong experience in an operational Linux environment and strong shell scripting skills.
  • Expertise in SQL for data extraction.
  • Experience with deep learning frameworks such as PyTorch, TensorFlow, Jax, Ray, etc., and accelerators (e.g., TPUs, GPUs).
  • Familiarity with model architectures like encoders, decoders, and transformers, and experience with APIs and frameworks for machine learning.

DESIRED QUALIFICATIONS:

  • Master’s degree or Ph.D. in Engineering, Computer Science, or a related technical field.
  • 5+ years of experience in a technical leadership role, setting the direction for project teams.
  • Recent hands-on experience in developing and delivering ML-based solutions in production.
  • Experience in power transmission and distribution systems, especially with time series data analysis and integration of renewable energy sources.
  • Strong knowledge of performance metrics, real-time data processing, and risk analysis.
Responsibilities
  • Design, develop, test, and maintain large-scale software systems, ensuring high standards of quality, performance, and security.
  • Lead the development and delivery of new features and advanced capabilities for our grid management platform.
  • Design, build, and deploy scalable, production-ready machine learning and AI-driven solutions.
  • Evaluate algorithm performance, scalability, and suitability for real-world applications.
  • Conduct model development, optimization, verification, and deployment processes.
  • Analyze and decompose complex business requirements to develop clear, executable solutions.
  • Stay at the forefront of industry developments, maintaining a strong knowledge base of the latest in machine learning best practices and evolving frameworks.
Loading...