Senior/Software Engineer (Cortex Code Agentic System)

• The Cortex Code team is building the future of coding agents for working with data. See our flagship product in action: Cortex Code in Action: Live Demos + AMA
• Your work will directly impact how developers and businesses build with data. You’ll own the full AI engineering lifecycle: design, prompt/tool engineering, evals, deployment, measurement, and optimization. You’ll work with a small, high-powered modeling and infrastructure team
• Own features end-to-end for Snowflake Cortex Code products. Build agentic workflows, coding harnesses, evaluation pipelines
• Build enterprise-grade context engineering: function calling, tool schemas, guardrails, agent teams, and verification/repair
• Partner with product and infra: translate customer problems into products and experiments. Collaborate with infrastructure teams to productionize improvements
• Work with an elite team of engineers towards building great products

Benefits

• Comprehensive health insurance plans
• Health savings accounts
• Robust retirement plans
• Life and disability insurance
• Weekly online lunch and learns
• Virtual workout classes
• Ergonomic work-from-home equipment
• On-demand mental health and wellness programs
• Fertility benefits and family planning resources
• Generous time-off and various leave plans
• Onsite and Remote Work
• Employee discounts and pre-tax selections
• New hire equity + Employee Stock Purchase Plan (ESPP)
• Quarterly bonus or commission program- 5+ years of experience shipping AI features in production
• Strong communication skills and ability to collaborate effectively in a team environment
• Bachelor’s degree in Computer Science, Engineering, Statistics or a related field. Master’s or higher degree preferred but not a requirement
• Proficiency in programming languages such as Python, Typescript, Go
• (Optional) Experience working with data engineering pipelines (dbt, airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus
• Background in data engineering (dbt, Airflow), data modeling, analytics, retrieval / RAG, or semantic layers — highly relevant for data-centric coding agents
• Deep experience with agentic coding tools (e.g. IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits
• Prior work on eval harnesses, LLM observability, or safety / guardrails in production
• Have built and owned complex systems — pipelines, orchestration, or software with substantial state, branching logic, and operational requirements
• Are a power user of modern coding agents and care about turning that intuition into systematic measurement and improvement
• Thrive in high-intensity environments with short feedback loops and high standards for rigor
• Take problems to completion independently: you don’t stop at a prototype; you care about production reliability and clear metrics
• Strong track record building and operating eval harnesses, measurement, and/or experimentation loops for LLM/agent systems—not only one-off benchmarks
• (Optional) Experience with data engineering pipelines (dbt, Airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus
• Proficiency in programming languages such as Python, TypeScript, Go (strong in at least two)
• Exceptional communication skills: crisp write-ups, constructive debate, and ability to influence without authority across engineering and product
• Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field. Master’s or higher preferred but not a requirement
• 5+ years of experience shipping AI/ML-backed software in production, including senior-level ownership of technical direction, cross-team delivery, and mentoring
• Have built and owned complex quality + data pipelines—substantial state, branching logic, and operational requirements
• Take ambiguous “quality is slipping” problems to completion: you care about clear metrics, reproducibility, and sustained improvement—not one-off score bumps
• Are a power user of modern coding agents and care about turning intuition into systematic measurement and team-wide practice