Data Scientist, AI/ML

About the Role

This opportunity is offered through a partner organization managing all the recruitment processes, aiming to onboard a Data Scientist specializing in AI/ML based in the United States. The position provides a platform to leverage sophisticated machine learning methodologies to enhance the reliability of extensive software infrastructures. You will convert vast sets of reliability experiment data into insightful solutions that detect failures, analyze their causes, and suggest corrective actions.

Situated at the confluence of artificial intelligence, distributed system technologies, and platform engineering, this role involves creating advanced reliability tools and collaborating with seasoned engineers and specialists to move research-driven models into live environments. Your contribution will be critical in assisting businesses to identify, understand, and avoid crucial system breakdowns.

Key Responsibilities

• Interpret massive datasets derived from reliability experiments to uncover failure trends, root causes, indicators of system robustness, and areas for enhancement.
• Design, develop, and refine machine learning models that autonomously detect and categorize failures within distributed infrastructures, providing explanations for their occurrence.
• Create intelligent systems that offer remediation advice and progressively automate reliability management.
• Develop scalable data pipelines and feature stores that support both machine learning training and real-time inference tasks.
• Utilize advanced AI methods, including causal inference, graph-based machine learning, time-series analysis, and reinforcement learning, to boost model accuracy and reliability insight generation.
• Work collaboratively with platform, software engineers, and reliability experts to deploy AI solutions in operational settings.
• Convert complex reliability data into user-friendly product features that enable customers to assess impact, identify causes, and speed recovery processes.
• Investigate and apply innovative machine learning, causal AI, and agentic AI techniques.
• Advocate for rigorous experimentation, thorough model assessment, and best practices in engineering throughout development cycles.

Candidate Requirements

• At least 5 years of relevant experience in building and deploying machine learning systems, preferably in domains such as distributed systems, infrastructure, DevOps, or reliability engineering.
• Strong programming skills with a track record of producing production-quality data and machine learning solutions.
• Practical experience with machine learning techniques like causal inference, graph machine learning, time-series models, or reinforcement learning.
• Proficiency in developing scalable data pipelines and feature stores catering to offline model training and online inference.
• Deep understanding of model evaluation strategies, experimental design, and machine learning engineering principles.
• Experience coordinating with cross-functional teams including platform engineers and Site Reliability Engineering (SRE) professionals to deliver production-ready features.
• Ability to decompose complex and unclear technical problems into structured tasks with measurable milestones.
• Excellent communication capabilities to clearly convey complex technical aspects to diverse audiences.
• Experience thriving in agile and remote-first work environments.
• A self-motivated approach with a strong sense of ownership, inquisitiveness, and problem-solving skills.

Preferred Qualifications

• Background or experience in chaos engineering, site reliability engineering, or distributed system environments.
• Experience with development of agentic AI systems or extensive causal inference solutions in production settings.
• Familiarity with MLOps practices including model deployment, monitoring, and feature infrastructure management.
• Participation in incident handling processes or on-call duties.

Compensation and Benefits

• Annual salary range between 220000 and 290000 USD, dependent on expertise, skills, and prevailing market conditions.
• Comprehensive total compensation inclusive of equity options.
• 401(k) plan with employer matching contributions.
• Flexible leave policy for time off.
• Paid holidays recognized by the company.
• Remote-first working environment promoting a cooperative engineering culture.
• Opportunity to contribute to transformative AI-driven reliability solutions impacting organizations reliant on high system availability.
• Career growth within a team dedicated to continuous learning, experimentation, and technical excellence.

Additional Information

The recruitment process involves an AI-supported matching mechanism to ensure applications are assessed swiftly and impartially against role prerequisites. Top candidates are forwarded directly to the hiring entity, which conducts all subsequent interview and selection stages.

All personal data provided during application will be handled in compliance with data protection regulations, including GDPR, and may be processed using AI tools to assist recruitment, though final hiring decisions remain human-led.