Applied Machine Learning
Build and ship applied machine learning features powered by generative and multimodal models, taking projects from experimentation and evaluation through production. The role requires backend Python experience, familiarity with PyTorch or JAX, and the ability to improve quality, latency, or cost.
About the job
Responsibilities
- Bridge research and product by turning generative models into production features for first-party applications and APIs.
- Experiment quickly, build evaluations and datasets, and collaborate with research, engineering, infrastructure, and clients.
- Fine-tune and deploy models for text-to-image, image-to-text, image enhancement and editing, and multimodal use cases.
- Define client success metrics across quality, latency, and cost.
- Contribute to system safety, monitoring, and reliability.
- Lead zero-to-one projects shaping applied machine learning practices.
- Take machine learning features from ideation through evaluation and production, delivering measurable improvements.
- Collaborate with product teams to define success criteria and iterate pragmatically.
- Curate, label, and clean datasets; design evaluation corpora and analyze failure modes.
Requirements
- 1+ years of experience building and shipping machine learning products.
- Backend programming experience with Python, PyTorch, and JAX.
- Ability to design benchmarks and evaluation metrics, conduct error analyses, and debug numerical stability across data, training, and inference.
- Familiarity with modern deep learning, including transformers, diffusion models, and encoders.
- Strong communication and interpersonal skills.
- Ownership, urgency, curiosity, and ability to learn quickly in a startup environment.
Benefits
- Competitive compensation and equity.
- Four weeks of vacation.
- Comprehensive health, vision, and dental coverage from day one.
- RRSP/401(k) with employer match of up to 4%.
- Top-of-the-line tools and technology.
- Toronto headquarters perks, including daily in-office lunches and dinners.
- Autonomy to explore and experiment with access to required compute and resources.
- Learning, growth, and mentorship opportunities.
Skills
Python, PyTorch, JAX, Transformers, Diffusion Models, Deep Learning, Machine Learning, Model Evaluation, Dataset Curation, Error Analysis, Numerical Stability, Multimodal Models
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