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Mechanical Reliability Engineer Jobs in Washington

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How much do mechanical reliability engineer jobs pay per year?

As of Sep 3, 2026, the average yearly pay for mechanical reliability engineer in Washington is $117,957.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $133,600.00 per year, depending on experience, location, and employer.

What does a mechanical reliability engineer do?

A Mechanical Reliability Engineer ensures the reliability, efficiency, and longevity of mechanical systems and equipment in industrial settings. They analyze failure data, perform root cause analysis, and develop maintenance strategies to prevent breakdowns. Their role often involves working with predictive maintenance technologies, performing risk assessments, and collaborating with other engineers to improve system performance. By optimizing reliability, they help reduce downtime, maintenance costs, and production disruptions.

What are the key skills and qualifications needed to thrive as a mechanical reliability engineer?

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Are reliability engineers in demand?

Reliability engineers are in high demand across industries such as manufacturing, energy, and aerospace due to the need to improve equipment performance and reduce downtime. They often require skills in data analysis, failure modes, and maintenance strategies, with job growth driven by a focus on operational efficiency and safety.

What are the most commonly searched types of Mechanical Reliability Engineer jobs in Washington?

The most popular types of Mechanical Reliability Engineer jobs in Washington are:

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For Mechanical Reliability Engineer jobs in Washington, the most frequently searched job titles are:

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The top searched job categories for Mechanical Reliability Engineer jobs in Washington are:

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Mechanical Reliability Engineer jobs near you
Infographic showing various Mechanical Reliability Engineer job openings in Washington as of August 2026, with employment types broken down into 86% Full Time, 6% Part Time, 2% Temporary, 5% Contract, and 1% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $117,957 per year, or $56.7 per hour.

Sr. Site Reliability Engineer

Tiger Analytics Inc.

Washington, DC • On-site

$64.50 - $85.75/hr

Full-time

Re-posted 28 days ago

Job description

Role Overview

We are seeking a high-caliber Site Reliability Engineer (SRE) to join our Forward Engineering team. You will be the guardian of our production ecosystems, ensuring that our complex, data-driven AI platforms remain resilient, scalable, and highly performant. This role is a hybrid of software engineering and systems architecture, with a specialized focus on MLOps—bridging the gap between model development and production-grade reliability.

Key Responsibilities1. Reliability & Performance Engineering
  • SLA/SLO Management: Define, monitor, and maintain Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for critical AI/ML services.
  • Error Budgeting: Manage error budgets to balance the velocity of feature releases from the ML team with the stability of the production environment.
  • Scalability: Architect and manage auto-scaling strategies for Kubernetes (GKE) to handle fluctuating workloads during model training and high-volume inference.
2. MLOps & AI Infrastructure
  • Model Serving Reliability: Ensure the high availability of Vertex AI endpoints and custom inference services.
  • GPU/TPU Optimization: Monitor and optimize compute resource utilization (accelerators) to ensure cost-efficient performance for Large Language Models (LLMs).
  • Pipeline Resilience: Support and stabilize ML pipelines (Vertex AI Pipelines/Kubeflow) to ensure seamless data flow from ingestion to model retraining.
3. Automation & Orchestration (Eliminating "Toil")
  • Infrastructure as Code (IaC): Use Terraform or Pulumi to provision and manage consistent, version-controlled cloud environments.
  • CI/CD & GitOps: Design and optimize robust deployment pipelines for both application code and ML models using GitHub Actions, Cloud Build, or ArgoCD.
  • Task Automation: Develop custom Python or Go scripts to automate repetitive operational tasks, self-healing mechanisms, and resource cleanup.
4. Monitoring, Alerting & Incident Response
  • Observability: Build and manage comprehensive dashboards using Prometheus, Grafana, or Google Cloud Operations Suite (Stackdriver).
  • Incident Management: Act as a primary responder in on-call rotations, leading the technical resolution of production outages.
  • Blameless Post-Mortems: Conduct deep-dive root cause analysis (RCA) to ensure systemic issues are identified and permanently remediated through code.

Requirements

Orchestration: Expert-level knowledge of Kubernetes (K8s) and Docker.

MLOps Stack: Familiarity with tools such as Kubeflow, Vertex AI, MLflow, or DVC.

Scripting: Strong proficiency in Python (for automation) and Bash; knowledge of Go is a plus.

Data Systems: Experience managing the reliability of data-heavy services (BigQuery, Pub/Sub, or Vector Databases like Pinecone/Milvus).

Networking: Solid understanding of VPCs, Load Balancers, DNS, and secure service mesh (Istio/Anthos).

Benefits

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.