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
- Staff ML engineer building models, evaluations, and agentic systems for Sourcegraph’s code-understanding products. Improving enterprise code search quality, latency, cost, and reliability.
Responsibilities
- Set the Code Understanding team's direction for models, evaluations, and agentic systems
- Design and harden multi-step, tool-using agent loops for reliable, observable, affordable enterprise-scale products
- Determine when and how to use evaluations, smoke tests, metrics, and qualitative review
- Select, upgrade, fine-tune, and train models where appropriate
- Improve retrieval, ranking, context windows, and citations for code-grounded answers
- Optimize model cost and latency through profiling, distillation, caching, and right-sizing
- Own meaningful agentic product slices end-to-end from problem framing through rollout and measurement
- Establish evaluations, dashboards, and guardrails for responsible model and prompt changes
- Mentor and up-level teammates in agent engineering
- Engage with customers and translate feedback into requirements, scopes, and milestones
- Contribute across the codebase and influence technical direction beyond the immediate team
- Participate in the on-call support rotation
- Drive roadmap direction and measurable improvements in answer quality, cost, latency, and agentic capabilities
Requirements
- Staff engineer and technical leader with production machine learning, evaluation, and agent systems expertise
- Personally owned a production model lifecycle from dataset construction through evaluation, production rollout, and monitoring
- Trained or fine-tuned at least one model
- Experience designing reliable, observable, and cost-bounded multi-step agentic systems
- Strong evaluation judgment, including representative datasets, baselines, error taxonomies, and release criteria
- Ability to make quality, latency, and cost tradeoffs using model selection, prompting, retrieval, caching, distillation, and fine-tuning
- Ability to operate autonomously on ambiguous, high-technical-risk problems
- Strong software engineering skills and ability to ship production services
- Comfortable across Go, TypeScript, GraphQL, Postgres, and Docker, or clearly able and eager to learn them
- Fluent with agentic coding tools and able to understand and own every line they submit
- Comfortable in an async-first, multi-service, fast-paced remote environment
- Ability to mentor engineers through pairing, design reviews, and code reviews
- Customer- and product-driven approach
- Working hours must overlap with EST for at least 20 hours per week
Benefits
- Meaningful equity
- Competitive cash compensation
- Generous perks and benefits
- Open and transparent compensation philosophy
- Pay bands designed for competitive and equitable compensation
- Globally distributed remote work arrangement
- Flexible location options in almost any part of the world
- Equal opportunity workplace
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