AI Scientist / Senior AI Scientist
Remote
Job Summary
Lead end-to-end AI initiatives from problem definition through production rollout, balancing model quality with operational constraints and measurable business outcomes. Design practical ML and agent-based solutions, establish evaluation frameworks linking offline metrics to KPI impact, and define model-operations playbooks for incident response and performance degradation. Collaborate closely with product, engineering, and data partners to align scope, sequencing, and accountability while driving iterative delivery with short feedback loops. Own high-leverage initiatives or a portfolio of opportunities across teams, setting prioritization and technical standards that others adopt. Launch capabilities in predictive ordering and agentic workflow management, resolving ambiguity under stakeholder leadership.
Required Qualifications
- Proven track record delivering AI/ML systems to production with measurable business outcomes
- Deep familiarity with current LLM and agent technologies, including practical evaluation and failure-mode handling
- Demonstrated ability to productionize complex models and model-adjacent systems with strong reliability and observability practices
- Heavy, day-to-day use of AI-native engineering workflows (coding, framing/design, debugging, and code review) for at least the past 18 months
- Working implementation proficiency across at least two technical ecosystems/cloud stacks (for example AWS and GCP)
- Strong quantitative foundation in experimentation, statistical reasoning, and model evaluation
- Strong collaboration skills; can drive alignment and decisions under ambiguity
- 5–7+ years in applied data science / machine learning roles with repeated production delivery
- Track record owning initiatives end-to-end—not only contributing to models owned by others
- Leadership-level influence within a cross-functional squad; improves team decision quality through technical rigor
- 8+ years in applied data science / machine learning roles with portfolio-level outcome ownership
- Track record owning AI/ML initiatives from concept through production and measurable business impact at cross-team scope
- Stakeholder leadership across product, data, engineering, and operations; can resolve prioritization under ambiguity
Desired Qualifications
- Experience implementing local/self-hosted AI solutions (for example self-managed agent infrastructure on-prem or in your own environment)
- Experience with retrieval systems, vector search, ranking/recommendation, or other production AI personalization workflows
- Experience in e-commerce, B2B vendor management, financial products, or external systems integrations
- Experience setting team-level standards for model governance, monitoring, and responsible AI practices
- Experience mentoring senior ICs and shaping cross-team technical direction
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