Senior ML Engineer

Location: Remote

Working timezone: Toronto / North America overlap (afternoon → late evening EU time);
Engagement: Full-time, long-term

We’re looking for a versatile and hands-on Machine Learning Engineer to join our client's team and take ownership of ML systems from early research through to real-world deployment. In this role, you’ll work across the full lifecycle - shaping datasets, exploring new approaches, building models, and running them reliably in production. T

his is a good fit if you enjoy fast iteration, practical experimentation, and seeing your work directly influence product outcomes.

Responsibilities:

• ML solution design: Explore, evaluate, and implement machine learning approaches to solve complex product and business challenges using modern model architectures.
• Experimentation workflows: Set up and maintain pipelines that support fast prototyping, testing ideas, and validating assumptions.
• Structured experimentation: Plan, run, and document experiments in a reproducible way, including tuning hyperparameters, comparing architectures, and testing data variants using tools like MLflow or Weights & Biases.
• Production rollout: Bring models into production using established CI/CD workflows and model serving infrastructure.
• Model observability: Build monitoring and alerting around deployed models to track accuracy, drift, latency, and overall system health.
• Inference optimization: Improve runtime performance and cost efficiency through techniques such as quantization, pruning, and distillation.
• Training data ownership: Create, refine, and maintain high-quality datasets, including data augmentation and curation strategies.
• Modern ML methods: Keep up with current advances in the field and apply state-of-the-art approaches, particularly in the area of large language models.

Requirements:

• 3+ years of hands-on experience building and deploying machine learning models in production systems.
• Strong Python skills and experience writing production-grade ML code.
• Solid experience with deep learning frameworks, especially PyTorch.
• Practical background in hyperparameter tuning and optimization using tools such as Optuna or Ray Tune.
• A disciplined approach to experimentation, with strong habits around reproducibility and tracking.
• Hands-on work with LLMs, including fine-tuning, prompt design, retrieval-augmented generation, and efficient inference.
• Experience applying model optimization techniques (e.g. quantization, pruning).
• Proven ability to design and build datasets rather than only consuming existing ones.
• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a closely related field.

Nice to have:

• Experience with advanced architectures such as Transformers or Mixture-of-Experts models.
• Contributions to open-source projects or personal ML projects that demonstrate curiosity and depth.
• Practical understanding of end-to-end MLOps, including tools like Docker, Kubernetes, MLflow, Kubeflow, or Prometheus.