Machine Learning Operation Manager

Role Overview
QuillBot is looking for a hands-on Machine Learning Operation Manager to lead and scale our AI Engineering & MLOps function. This role blends deep technical execution (60%) with team and cross-functional collaboration (40%), and is ideal for someone who thrives in a dual IC + strategic lead position.

You'll work closely with Research, Platform, Infra, and Product teams — not only to deploy models reliably, but also to accelerate experimentation, training, and iteration cycles. From infra support for large-scale model training to scaling low-latency inference systems in production, you'll be at the heart of how AI ships at QuillBot.

Responsibilities

• Own the full ML lifecycle: from training infra and experiment tracking to deployment, observability, and optimization.
• Work closely with researchers to remove friction in training, evaluation, and finetuning workflows.
• Guide and mentor a small, mature team of engineers (3–4), while still contributing as an individual contributor.
• Drive performance optimization (latency, throughput, cost efficiency), model packaging, and runtime reliability.
• Build robust systems for CI/CD, versioning, rollback, A/B testing, monitoring, and alerting.
• Ensure scalable, secure, and compliant AI infrastructure across training and inference environments.
• Collaborate with cloud and AI providers (e.g., AWS, GCP, OpenAI) as needed to integrate tooling, optimize costs, and unlock platform capabilities.
• Contribute to other GenAI and cross-functional AI initiatives as needed, beyond core MLOps responsibilities.
• Contribute to architectural decisions, roadmap planning, and documentation of our AI engineering stack.
• Champion automation, DevOps/MLOps best practices, and technical excellence across the ML lifecycle.

Qualifications

• 5+ years of strong experience in MLOps, ML/AI Engineering.
• Solid understanding of ML/DL fundamentals and applied experience in model deployment and training infra.
• Proficient with cloud-native ML tooling (e.g., GCP, Vertex AI, Kubernetes).
• Comfortable working on both training-side infra and inference-side systems.
• Good to have experience with model optimization techniques (e.g., quantization, distillation, FasterTransformer, TensorRT-LLM).
• Proven ability to lead complex technical projects end-to-end with minimal oversight.
• Strong collaboration and communication skills — able to work cross-functionally and drive technical clarity.
• Ownership mindset — comfortable making decisions and guiding others in ambiguous problem spaces."

Benefits & Perks

• Competitive salary and annual bonus
• Medical coverage
• Life and accidental insurance
• Vacation & leaves of absence (menstrual, flexible, special, and more!)
• Developmental opportunities through education & developmental reimbursements & professional workshops
• Maternity & parental leave
• Hybrid & remote model with flexible working hours
• On-site & remote company events throughout the year
• Tech & WFH stipends & new hire allowances
• Employee referral program
• Premium access to QuillBot

*Benefits and benefit amounts differ by region. A comprehensive list applicable to your region will be provided in your interview process.

Research shows that candidates from underrepresented backgrounds often don't apply for roles if they don't meet all the criteria. We strongly encourage you to apply if you're interested: we'd love to learn how you can amplify our team with your unique experience!

This role is eligible for hire in India.

We are a virtual-first company and have employees dispersed throughout the United States, Canada, India and the Netherlands. We have a market-based pay structure that varies by location. The base pay for this position is dependent on multiple factors, including candidate experience and expertise, and may vary from the amounts listed. You may also be eligible to participate in our bonus program and may be offered benefits, and other types of compensation.

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