Senior Machine Learning Engineer

This is a Full Time Job

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Summary

The Senior Tactical ML Engineer is a senior machine learning engineer in charge of resolving high-impact issues across research, evaluation, and integration environments. This role focuses on diagnosing and stabilizing machine learning systems that are blocked, degraded, or at risk of failure.

Operating across model behavior, training dynamics, data quality, evaluation systems, infrastructure, and integration boundaries, the Tactical ML Engineer identifies root causes, implements targeted solutions, and restores forward progress. The role emphasizes rapid intervention, disciplined debugging, and durable system improvements.

This position operates across teams and initiatives, transitioning ownership back to the responsible group once systems are stable and a clear path forward is established.

Key Responsibilities

System Triage & Root-Cause Resolution
• Perform rapid diagnosis across model, data, code, infrastructure, and evaluation layers for blocked or unstable efforts.
• Identify root causes and define corrective actions required to restore progress.
• Communicate findings and resolution plans clearly across research, engineering, and operational teams.

Targeted ML System Intervention
• Contribute directly to blocked ML initiatives by implementing fixes across model behavior, data pipelines, and system architecture.
• Develop and validate solutions, including debugging, targeted refactoring, and experimental validation.
• Build enabling components or modifications required to unblock downstream development.

Stabilization & Handoff
• Ensure that resolved systems are stable, validated, and ready for continued development.
• Provide clear handoff artifacts, including working code, documentation, and recommended next steps.
• Establish preventative measures to reduce recurrence of identified issues.

Cross-Functional Alignment
• Work across research, infrastructure, platform, evaluation, and integration teams to align on root causes and resolution plans.
• Ensure that fixes are compatible with downstream systems and integration requirements.
• Validate that changes do not introduce regressions through appropriate testing and benchmarking.

Accountabilities
• Resolution efficiency: High-impact issues are resolved with clear root-cause identification and durable fixes.
• Execution pace: Time from escalation to restored progress is minimized without compromising quality.
• System stability: Resolved systems maintain reliability and do not regress under continued use.
• Knowledge transfer: Owning teams receive sufficient context, documentation, and artifacts to continue development autonomously.

Required Qualifications
• Senior-level experience spanning software engineering, machine learning systems, and infrastructure in production or production-adjacent environments.
• Solid debugging capability across multiple system layers, including application code, data pipelines, distributed training, and deployment systems.
• Experience diagnosing and resolving complex issues in ML systems under time constraints.
• Well-developed operational judgment, including the ability to triage, prioritize, and execute with incomplete information.
• Effective communication skills and ability to collaborate across multiple technical disciplines.

Core Competencies
• Technical breadth: Ability to operate across models, data systems, and infrastructure.
• Root-cause analysis: Solid discipline in identifying underlying causes across system boundaries.
• Execution under tension: Ability to stabilize systems and drive resolution in high-impact scenarios.
• Cross-functional collaboration: Effective coordination across research, engineering, and integration teams.
• Durable problem solving: Focus on long-term fixes rather than temporary solutions.