Senior Machine Learning Engineer

Role Description

We are looking for a Senior Machine Learning Engineer to join the Innovation team and work on our most ambitious technical initiatives to date. You will be a core member of a small, high-ownership team building and fine-tuning LLMs utilizing the unique dataset that Power Factors has access to. You will work across data preparation, architecture-to-business-value alignment, curriculum strategies, tokenization strategies, and scaling models from PoC to production grade.

The Role — What You'll Do

• Architecture & Research
  • Own architecture decisions for PF's foundational time-series model: head choice, attention variant, context/horizon sizing, and multi-frequency handling — grounded in empirical evidence from the pilot dataset.
  • Design the tokenization strategy for PF's multi-modal training corpus: quantization scheme, multi-frequency handling, and event/metadata interleaving.
  • Establish scaling-law estimates on the pilot dataset to project fleet-scale capacity requirements.
  • Track time-series foundation model literature and translate relevant findings into the PF training context.
  • Design an experimental framework to validate business value for target use cases.
• Training Infrastructure
  • Build and maintain a containerized, reproducible training environment with full experiment tracking and baseline comparisons.
  • Optimize the distributed training pipeline: throughput, memory layout, gradient accumulation, checkpointing, and fault recovery.
  • Design and implement the model registry, linking config, metrics, dataset version, and code SHA for every artifact.
  • Own the pre-training recipe: learning rate schedules, masking/curriculum strategies, and validation protocols.
• Fleet-Scale Execution
  • Scale training from pilot to the full ~1,000-site universe, including per-asset-class and per-OEM normalization.
  • Handle real-world data quality issues: outliers, flatlines, missing sensors, and irregular sampling.
  • Report baseline metrics per asset class, OEM, and capacity bucket; iterate based on shadow validation.
• Collaboration & Knowledge
  • Partner with the Backend/Data Engineer on data quality standards, feature store design, and pipeline interfaces.
  • Collaborate with the Tech Lead and Product team to ensure model outputs meet pilot customer requirements.
  • Document training runbooks, debugging procedures, and architecture decisions to enable team-wide operability.

Qualifications

• 5+ years of ML engineering experience, with meaningful time in foundation model or large-scale model development.
• Deep expertise in time-series modelling — multivariate, multi-frequency, and heterogeneous sensor data.
• Proven ability to design and train transformer-based or sequence model architectures from scratch.
• Distributed training engineering: GPU cluster config, mixed-precision training, gradient accumulation, checkpointing, and fault recovery.
• Tokenization and representation design for continuous time-series data: quantization, patching, and event/metadata interleaving.
• Strong Python and PyTorch (or JAX) skills; proficiency with the HuggingFace ecosystem.
• MLOps fluency: experiment tracking, model registry design, reproducible pipelines, and automated retraining.
• Excellent written and verbal English communication skills.

Beneficial Qualifications

• Familiarity with published time-series foundation model approaches (Chronos, Moirai, TimesFM, or similar) — a significant advantage.
• Experience with uncertainty quantification in forecasting: Gaussian, mixture, or quantile output heads.
• Background in scaling-law estimation for model capacity planning.
• Exposure to multi-modal training corpora combining continuous signals, discrete operational events, and structured metadata.
• Renewable energy, SCADA, or industrial IoT data experience — including an understanding of signal quality issues (sparsity, flatlines, sensor drift) in real-world deployments.
• Experience evaluating and selecting attention variants and context/horizon sizing for long-sequence tasks.
• Published research, open-source contributions, or patents in time-series modelling or foundation models.
• Knowledge of curriculum learning and masking strategies for pre-training.

Benefits

• Comprehensive benefits package including health, dental, and vision coverage, plus dedicated wellness support.
• Generous paid vacation policy.
• Employer RRSP matching program.
• Work-from-abroad opportunities with manager approval.
• Exposure to a global team operating across multiple countries and time zones.