Cinder

Artificial Intelligence

• Own the operational health, delivery, and outcomes for all Managed Services customers.
• Lead quality programs across customers, including QA design, precision/recall analysis, accuracy tracking, and continuous improvement.
• Identify root causes of quality regressions, missed SLAs, or labeling confusion — and drive fixes across tooling, training, or process.
• Own escalation and risk tradeoffs when quality, safety, or customer timelines are at risk, exercising sound judgment in high-pressure situations.
• Manage day-to-day relationships with BPO and data labeling partners.
• Partner with vendors on staffing models, headcount planning, efficiency improvements, and performance management across time zones.
• Review training materials, calibration processes, and reviewer feedback loops to ensure consistent, high-quality output at scale.
• Write, test, and iterate labeling and review instructions in partnership with Product and customers.
• Lead calibration processes with BPO partners to ensure consistent interpretation of guidelines and edge cases.
• Work closely with Product and Engineering on AI deployments that support Managed Services customers.
• Help design and refine workflows where AI augments — rather than replaces — expert human judgment.
• Use operational data to inform model iteration, tooling improvements, and future product decisions.
• Monitor pipeline health across human and AI-assisted workflows, identifying bottlenecks and making informed tradeoffs between quality, speed, and cost.
• Act as a primary operational point of contact for Managed Services customers.
• Navigate customer pressure around deadlines, errors, quality issues, and evolving requirements with calm judgment and clear communication.
• Own prioritization and decision-making when information is incomplete and trade-offs are real.
• Partner with GTM teams to bring Managed Services offerings to market.
• Support upsells, renewals, and new customer conversations by translating operational credibility into clear value.

AI Data Operations Lead

DevOps Engineer

• Drive our production infrastructure from Day 0 through Day 2 operations, continuously evolving it into a highly reliable platform.
• Build proactive deployment observability and guardrails that surface risk early, prevent outages, and continuously raise the bar on release safety.
• Architect and oversee compliance-by-design infrastructure, ensuring SOC 2, HIPAA, and GDPR requirements are embedded into systems.
• Partner across data, AI, and product to unblock work, improve velocity, and raise operational standards.
• Partner directly with our most strategic customers to diagnose issues, guide deployments, and shape infrastructure decisions that matter in production.
• Contribute to on-call coverage while serving as the go-to escalation point for complex deployment issues.

Site Reliability Engineer

AI Engineer

• Own the complete lifecycle of large language model implementation: from data preparation and fine-tuning through rigorous evaluation and production deployment.
• Develop automated evaluation frameworks that continuously assess model accuracy, identify edge cases, and quantify improvements across iterations.
• Work directly with product managers and engineers to integrate AI as a core product capability.
• Shape our AI roadmap by staying current with industry developments, evaluating emerging techniques, and making pragmatic adoption decisions.
• Design and implement low-latency, high-throughput, cloud-based AI/ML systems capable of handling thousands of requests per second
• Build the foundational infrastructure - model serving, monitoring, deployment pipelines, and automated testing frameworks - that enables rapid experimentation and iteration while maintaining production reliability.