Data Scientist, AI/ML

About This Opportunity

We are representing a confidential enterprise software organization that is transforming how development and operations teams build resilient, high-availability systems. Our client works with hundreds of leading organizations where uptime and reliability are mission-critical.

As a Data Scientist specializing in AI/ML, you will apply machine learning to analyze millions of system tests and experiments, turning complex failure data into automated insights and remediation strategies. This role offers the chance to work on novel ML challenges at the intersection of infrastructure, distributed systems, and reliability engineering.

Responsibilities

• Analyze proprietary datasets of system experiments to identify failure patterns, root causes, and resilience signals across distributed architectures
• Build and fine-tune machine learning models that automatically detect, classify, and explain failures observed during reliability testing
• Design intelligent systems that deliver automated remediation recommendations by learning from historical outcomes and system behavior
• Develop scalable data pipelines and feature stores to process large volumes of experiment data for model training and real-time inference
• Collaborate with platform engineers and SREs to integrate AI-driven failure analysis capabilities into core product features
• Apply advanced techniques including causal inference, graph ML, time-series modeling, and reinforcement learning to improve automated failure analysis
• Translate insights from experiment data into AI-powered features that help users understand blast radius, pinpoint root causes, and accelerate recovery
• Research and productionize novel ML approaches, including causal AI and agentic systems, for automated remediation strategies

What We're Looking For

Required qualifications:

• 5+ years of professional experience building and productionizing machine learning models, ideally for distributed systems, infrastructure, or DevOps/SRE use cases
• Additional years of overall software development experience beyond ML-specific work
• Hands-on experience with causal inference, graph ML, time-series modeling, or reinforcement learning
• Proven track record building data pipelines and feature stores supporting both offline training and real-time inference
• Experience in agile development environments and practices
• Strong advocate of rigorous experimentation, model evaluation, and engineering best practices
• Comfort partnering with engineering teams to turn research into shipped product features
• Ability to break down ambiguous problems into concrete actions and milestones
• Self-driven problem solver with strong communication and collaboration skills

Bonus experience:

• Background in site reliability engineering or distributed systems
• Experience with agentic AI systems or large-scale causal inference in production
• Experience deploying MLOps tooling such as model serving, monitoring, or feature store infrastructure
• Track record working in remote-first environments
• Experience participating in on-call rotations and incident management programs

Please note: This role does not offer visa sponsorship.

How We Work With You

Our talent team at Get A Job.ai partners with leading technology organizations to connect them with exceptional candidates. When you apply through our platform, a dedicated recruiter will review your background and conduct an initial screening. If there's a strong match, we will submit your profile to our client for consideration. Please do not contact the client directly—all communication and interview coordination will flow through Get A Job.ai to ensure the best experience for everyone involved.

Pay

Our client has indicated a salary range of $220,000 - $290,000 for this position, with final compensation based on experience, skills assessment, and market data. The role also includes equity, 401k matching, flexible time off, and other comprehensive benefits.

Get A Job.ai is an equal opportunity recruiter. We welcome candidates of all backgrounds and encourage you to apply even if you don't meet every qualification listed—we value candidates who are particularly strong in key areas and demonstrate interest and capability across others.