Job Description/Preferred QualificationsWe seek a highly skilled and passionate Senior AI Ops Engineer to join our team. This role will be pivotal in architecting and delivering the automation layer that enables fast, reproducible, and scalable model development-spanning end-to-end experiment management, model fine-tuning pipelines, and Reinforcement Learning with Human Feedback (RLHF). We encourage you to apply if you're a systems-minded engineer who loves turning research workflows into reliable production-grade pipelines, setting standards, and mentoring others to raise the bar across the organization.
Key Responsibilities:- Implement and operate experiment tracking, lineage, and reproducibility standards (datasets, code, configs, artifacts, metrics) using MLflow/W&B or equivalents.
- Build CI/CD for ML: tests (unit/integration), packaging, reproducibility checks, policy gates, automated deployment and rollback strategies.
- Design workflow orchestration for large-scale ML jobs (scheduled runs, triggered retrains, parameter sweeps, gated releases) using tools such as Airflow/Kubeflow/Argo or equivalents.
- Architect, build, and own automated pipelines for model training, fine-tuning (e.g., PEFT/LoRA), evaluation, and promotion across environments (dev 12 staging 12 production).
- Establish standardized training "recipes" (configs, templates, golden paths) to reduce time-to-first-experiment and improve consistency across teams.
- Enable and optimize distributed GPU training (throughput, reliability, and cost), including checkpointing, mixed precision, fault tolerance, and spot/preemptible handling where applicable.
- Develop evaluation harnesses and automated benchmark suites (quality, safety, latency, and cost) with clear, repeatable reporting to compare runs and releases.
Qualifications:- Strong proficiency in Python and experience building robust automation frameworks and production-grade services for ML workloads
- Hands-on experience with experiment tracking and model lifecycle tooling (e.g., MLflow, Weights & Biases) and reproducible ML workflows
- Practical experience fine-tuning modern deep learning models (e.g., Transformers) and familiarity with parameter-efficient approaches (LoRA/PEFT)
- Working knowledge of RLHF concepts and pipelines (preference data, reward models, policy optimization) and how to operationalize human-in-the-loop workflows.
- Experience with containerization (Docker), orchestration (Kubernetes), and operating GPU workloads reliably at scale.
- Experience with CI/CD, version control (Git), and Infrastructure-as-Code (Terraform/Bicep or equivalent).
- Excellent problem-solving skills across distributed systems (training jobs, pipelines, compute infrastructure) and strong communication to partner with research and engineering teams.
- Prior experience in a similar industry and/or operating ML platforms with stringent IP/security requirements is a plus.
- Bachelor's degree in Computer Science, Software Engineering, or related field
- 5+ years of experience in MLOps/Platform Engineering/DevOps/ML Engineering (or demonstrated equivalent impact), including owning production systems and leading cross-team initiatives
Minimum QualificationsDoctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years
Base Pay Range: $111,900.00 - $190,200.00 Annually
Primary Location: USA-MI-Ann Arbor-KLA
KLA's total rewards package for employees may also include participation in performance incentive programs and eligibility for additional benefits including but not limited to: medical, dental, vision, life, and other voluntary benefits, 401(K) including company matching, employee stock purchase program (ESPP), student debt assistance, tuition reimbursement program, development and career growth opportunities and programs, financial planning benefits, wellness benefits including an employee assistance program (EAP), paid time off and paid company holidays, and family care and bonding leave.
Interns are eligible for some of the benefits listed. Our pay ranges are determined by role, level, and location. The range displayed reflects the pay for this position in the primary location identified in this posting. Actual pay depends on several factors, including state minimum pay wage rates, location, job-related skills, experience, and relevant education level or training. We are committed to complying with all applicable federal and state minimum wage requirements where applicable. If applicable, your recruiter can share more about the specific pay range for your preferred location during the hiring process.
