Machine Learning Engineer

What you'll do

• Implement and benchmark ML, NLP, and generative-AI methods, including LLM fine-tuning, retrieval-augmented generation, and document understanding.
• Prepare case data, build reusable datasets, and run experiments and A/B tests to assess model performance.
• Help integrate, test, monitor, and scale models in production in collaboration with product, engineering, and legal teams.

What they ask for

• Solid grounding in ML fundamentals, including supervised and unsupervised learning, evaluation metrics, overfitting, and regularization.
• Hands-on experience with NLP or generative-AI techniques such as transformers, embeddings, sequence-to-sequence models, or LLMs.
• Proficiency in Python and ML/NLP libraries such as PyTorch, TensorFlow, Hugging Face, or spaCy.
• Familiarity with SQL and basic data-engineering concepts; able to work hybrid three days of your choice in San Francisco or Toronto.

Nice to have

• Exposure to AWS or GCP, experiment-tracking tools, or containerized deployment.
• Experience in a high-growth startup environment.

Pay

$130K - $205K (currency and pay basis not stated)

Tools

Python, PyTorch, TensorFlow, Hugging Face, spaCy, SQL, AWS, GCP

AI focus

Develop and deploy machine-learning, NLP, and generative-AI models; explore LLM fine-tuning, retrieval-augmented generation, document understanding, representation learning, and prompt engineering.