This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist, AI/ML based in United States.

This role offers the opportunity to apply advanced machine learning techniques to improve the reliability of large-scale software systems.
You will transform millions of reliability experiments into intelligent solutions that identify failures, uncover root causes, and recommend remediation strategies.
Working at the intersection of AI, distributed systems, and platform engineering, you will help build next-generation reliability capabilities.
You will collaborate with experienced engineers and reliability specialists to bring research-driven models into production.
Your work will directly influence how organizations detect, understand, and prevent critical system failures.
This is an impactful opportunity for a data scientist passionate about applied AI, experimentation, and solving complex technical challenges.

As a Data Scientist, AI/ML, you will design, build, and productionize machine learning solutions that analyze complex system behaviors and improve reliability outcomes. You will combine research, engineering, and product thinking to create AI-powered capabilities that deliver measurable value to customers.

• Analyze large-scale datasets from reliability experiments to identify failure patterns, root causes, resilience indicators, and opportunities for improvement.
• Develop, train, and fine-tune machine learning models that automatically detect, classify, and explain failures within distributed systems.
• Build intelligent systems that generate remediation recommendations and evolve toward automated reliability orchestration.
• Create scalable data pipelines, feature stores, and infrastructure supporting both machine learning training and real-time inference.
• Apply advanced AI techniques such as causal inference, graph machine learning, time-series modeling, and reinforcement learning to improve model performance and reliability insights.
• Collaborate closely with platform engineers, software engineers, and reliability specialists to integrate AI capabilities into production environments.
• Translate complex reliability data into actionable product features that help customers understand impact, identify root causes, and accelerate recovery.
• Research and implement emerging approaches in machine learning, causal AI, and agentic AI systems.
• Promote strong experimentation practices, rigorous model evaluation, and engineering best practices throughout development cycles.

The ideal candidate is a highly technical and collaborative data scientist with experience building production machine learning systems. You should be comfortable working on ambiguous problems, partnering with engineering teams, and applying AI techniques to real-world infrastructure challenges.

• 5+ years of professional experience building and deploying machine learning solutions, ideally within distributed systems, infrastructure, DevOps, or reliability-focused environments.
• Strong software development background with experience designing production-quality data and machine learning systems.
• Hands-on experience with machine learning approaches such as causal inference, graph ML, time-series modeling, or reinforcement learning.
• Experience building scalable data pipelines and feature stores for offline model training and real-time inference.
• Strong understanding of model evaluation, experimentation methodologies, and machine learning engineering practices.
• Experience collaborating with platform engineers, SRE teams, and cross-functional stakeholders to deliver production features.
• Ability to break down complex and ambiguous technical problems into structured solutions and measurable milestones.
• Strong communication skills with the ability to explain technical concepts clearly to both technical and non-technical audiences.
• Experience working effectively in agile, remote-first environments.
• Self-driven mindset with strong ownership, curiosity, and problem-solving abilities.

Preferred Qualifications:

• Experience with chaos engineering, site reliability engineering, or distributed systems.
• Background building agentic AI systems or large-scale causal inference solutions in production.
• Experience implementing MLOps capabilities such as model serving, monitoring, or feature infrastructure.
• Experience participating in incident response processes or on-call rotations.

• Competitive salary range of $220,000 – $290,000 annually, depending on experience, skills, and market factors.
• Competitive total compensation package including equity opportunities.
• 401(k) matching program.
• Flexible time off policy.
• Paid company holidays.
• Opportunity to work in a remote-first environment with a collaborative engineering culture.
• Ability to contribute to innovative AI solutions used by organizations that rely on highly available systems.
• Growth opportunities within a team focused on learning, experimentation, and technical excellence