Applied Machine Learning Scientist developing Generative AI and predictive ML solutions at TD, a major North American bank. Supporting model evaluation, deployment, monitoring, and responsible AI governance.
About the role
- Senior Machine Learning Engineer building conversational AI agents and production ML systems. Helping Numa automate automotive dealership service and sales through evaluation-first tooling and infrastructure.
Responsibilities
- Build conversational AI systems for phone and SMS that understand customer needs, take action, and determine when to act autonomously
- Develop memory, knowledge graphs, and validated customization tooling for dealership-focused agents
- Train, evaluate, and deploy ML models using Ray Serve and Dagster for prediction, classification, ranking, and capacity forecasting
- Maintain ML models in production
- Create offline and online evaluations, simulations, and CI gates to measure quality and catch regressions
- Implement shared evaluation, observability, and LLM tooling
- Contribute to model serving, LLM infrastructure, and production observability
- Raise engineering standards through design and code reviews, technical writing, and mentorship
- Work autonomously and help create clarity in ambiguous situations
- Ship AI features including prompts, agents, tools, and production ML models that interact with real customers
Requirements
- 6+ years of software or machine learning engineering experience
- Track record of shipping ML or LLM powered systems to production
- Strong Python and solid software engineering fundamentals
- Hands-on experience building AI systems
- Experience training and deploying ML models for prediction, classification, and ranking and/or working with LLMs, prompting, tool use, agents, and retrieval
- Evaluation-first approach and ability to use measurement to assess improvements and regressions
- Comfort operating with meaningful ambiguity in a product environment
- Ownership and communication skills to carry work from problem framing through shipping, monitoring, and improvement
- Nice to have: ML platform and tooling experience, including evaluation frameworks, model serving, feature/prompt registries, or ML observability
- Nice to have: Familiarity with LiveKit, Ray Serve, Dagster, Vertex AI, GCP, Kubernetes, Pulumi, Anthropic, OpenAI, Deepgram, or ElevenLabs
- Nice to have: Real-time or streaming systems experience, including voice, SIP/WebRTC, or low-latency inference
- Nice to have: Startup or high-growth environment experience
Benefits
- Equity Packages
- Flexible PTO
- Fully Covered Group Insurance
- Opportunities for career advancement
- Everyone's growth
- Category-defining AI technology and industry leadership exposure
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Applied Machine Learning Scientist developing Generative AI and predictive ML solutions for TD banking. Evaluating models, managing AI lifecycles, and supporting responsible implementation.
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