Senior Machine Learning Engineer - Power Factors

About Power Factors

Power Factors is accelerating the green energy transition by providing advanced analytics and AI insights to operators of renewable energy assets. Our SaaS platforms are used to manage over 250 GW of wind, solar, hydro, and energy storage projects globally. By driving down operational costs and increasing revenue, we are tackling one of the world's most important challenges: making renewable energy the world's leading source of power. Our vision is to create a sustainable world powered by renewable energy. Our mission is to fight climate change with code.

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.

Must-Have 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.

What We Offer

• 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

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