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Freelance Mechanical Reliability Engineer Jobs in Springfield, VA

Govcio LLC

Senior Site Reliability Engineer

Arlington, VA · On-site

$210K - $230K/yr

... mechanisms • Document recovery time objectives (RTO) and recovery point objectives (RPO) Cloud ... E practices and tools • Document systems, processes, and runbooks • Drive continuous ...

Govcio LLC

Senior Site Reliability Engineer

Arlington, VA · On-site

$210K - $230K/yr

... mechanisms • Document recovery time objectives (RTO) and recovery point objectives (RPO) Cloud ... E practices and tools • Document systems, processes, and runbooks • Drive continuous ...

Govcio LLC

Senior Site Reliability Engineer

Crystal, VA · On-site

$210K - $230K/yr

... mechanisms • Document recovery time objectives (RTO) and recovery point objectives (RPO) Cloud ... E practices and tools • Document systems, processes, and runbooks • Drive continuous ...

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All JobsMechanical Reliability Engineer JobsFreelance Mechanical Reliability Engineer JobsFreelance Mechanical Reliability Engineer Jobs in Springfield, VA

Freelance Mechanical Reliability Engineer information

See Springfield, VA salary details

$74.7K

$108.8K

$151.5K

How much do freelance mechanical reliability engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for freelance mechanical reliability engineer in Springfield, VA is $108,785.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,400.00 and $123,300.00 per year, depending on experience, location, and employer.

What is the difference between Freelance Mechanical Reliability Engineer vs Mechanical Maintenance Engineer?

AspectFreelance Mechanical Reliability EngineerMechanical Maintenance Engineer
CredentialsRelevant engineering degrees, certifications in reliability or maintenanceTechnical diploma or associate degree, certifications in maintenance
Work EnvironmentContract-based, project-specific, often remote or on-site at various locationsOn-site at manufacturing or industrial facilities, regular shifts
Employer & IndustryClients across industries like oil & gas, manufacturing, energy; self-employed or consulting firmsManufacturing plants, factories, industrial facilities

Freelance Mechanical Reliability Engineers focus on analyzing and improving equipment reliability on a contract basis, often working across multiple projects and industries. Mechanical Maintenance Engineers handle routine and preventive maintenance tasks on-site to ensure equipment operates smoothly. While both roles require technical expertise, the freelance reliability engineer emphasizes analysis and optimization, whereas the maintenance engineer concentrates on day-to-day repairs and upkeep.

What are popular job titles related to Freelance Mechanical Reliability Engineer jobs in Springfield, VA?

For Freelance Mechanical Reliability Engineer jobs in Springfield, VA, the most frequently searched job titles are:

What cities near Springfield, VA are hiring for Freelance Mechanical Reliability Engineer jobs?

Cities near Springfield, VA with the most Freelance Mechanical Reliability Engineer job openings:

Freelance Mechanical Reliability Engineer jobs near you
Infographic showing various Freelance Mechanical Reliability Engineer job openings in Springfield, VA as of August 2026, with employment types broken down into 85% Full Time, 12% Part Time, 2% Contract, and 1% Nights. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $108,785 per year, or $52.3 per hour.

Sr. Site Reliability Engineer

Tiger Analytics Inc.

Washington, DC • Hybrid

$64.50 - $85.75/hr

Full-time

Re-posted 2 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.

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