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Remote Amazon Data Annotation Jobs in Denver, CO

Principal Ground Systems Integrator

Denver, CO · On-site +1

$120K - $160K/yr

TS/SCI Potential for Remote Work: ORA_ON_SITE Description SAIC is seeking experienced Principal ... Experience with Cloud-based systems deployed in Amazon Commercial Cloud Services (C2S) such as the ...

New

TS/SCI Potential for Remote Work: ORA_ON_SITE Description SAIC is seeking experienced Senior ... Experience with Cloud-based satellite ground systems deployed in Amazon Commercial Cloud Services ...

New

Lead AI Engineer - AWS Platform

Denver, CO · On-site +1

$130K - $190K/yr

Information Technology We are modernizing our data and analytics ecosystem by embedding AI and ... Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services * Contribute to evolving use of ...

Showing results 21-26

Remote Amazon Data Annotation information

What is the difference between Remote Amazon Data Annotation vs Remote Mechanical Turk Worker?

AspectRemote Amazon Data AnnotationRemote Mechanical Turk Worker
CredentialsNo formal certifications required, but attention to detail helpsNo formal certifications required, basic task understanding needed
Work EnvironmentRemote, flexible hours, online platformRemote, flexible hours, online micro-task platform
Employer & IndustryAmazon, e-commerce, AI training dataVarious clients, data labeling, surveys, research

Remote Amazon Data Annotation involves labeling data specifically for Amazon's AI and e-commerce needs, often requiring attention to detail. Mechanical Turk workers perform a variety of micro-tasks across industries. While both are remote and flexible, data annotation is more specialized for AI training, whereas Mechanical Turk offers broader task types.

What are common challenges faced by remote Amazon data annotation specialists and how can they be addressed?

Remote Amazon Data Annotation specialists often encounter challenges such as maintaining consistency and accuracy across large volumes of data, managing repetitive tasks, and staying engaged while working independently. To address these, it's important to develop a strong attention to detail, utilize quality control tools provided by the platform, and take regular breaks to minimize fatigue. Additionally, staying connected with your team through regular check-ins and feedback sessions can help ensure alignment on annotation guidelines and improve overall performance.

What is a remote Amazon data annotation job?

Remote Amazon Data Annotation jobs involve labeling, categorizing, or tagging data such as images, text, or audio to help train machine learning models used by Amazon. Employees work from home using specialized tools to ensure accuracy and consistency in the data provided. These roles often require attention to detail, the ability to follow guidelines, and sometimes specific domain knowledge depending on the project. Data annotation is essential for improving the performance of AI systems in tasks like product recommendations, voice recognition, and search algorithms. These roles may be full-time, part-time, or project-based, offering flexibility for remote workers.

What skills and qualifications are needed for a remote Amazon data annotation specialist?

To thrive as a Remote Amazon Data Annotation Specialist, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a high school diploma or relevant experience. Competence with web-based annotation tools, cloud-based platforms, and sometimes Amazon-specific data systems is typically required. Diligence, consistency, effective communication, and the ability to work independently are valuable soft skills in this role. These skills and qualities are important to ensure high-quality, accurate data labeling that supports effective machine learning and AI model development.
What are the most commonly searched types of Amazon Data Annotation jobs in Denver, CO? The most popular types of Amazon Data Annotation jobs in Denver, CO are:
What are popular job titles related to Remote Amazon Data Annotation jobs in Denver, CO? For Remote Amazon Data Annotation jobs in Denver, CO, the most frequently searched job titles are:
What job categories do people searching Remote Amazon Data Annotation jobs in Denver, CO look for? The top searched job categories for Remote Amazon Data Annotation jobs in Denver, CO are:
What cities near Denver, CO are hiring for Remote Amazon Data Annotation jobs? Cities near Denver, CO with the most Remote Amazon Data Annotation job openings:
Infographic showing various Remote Amazon Data Annotation job openings in Denver, CO as of June 2026, with employment types broken down into 68% Full Time, 24% Part Time, and 8% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.

Systems Software Engineer - Object Storage

Quantum US

Englewood, CO • Remote

$130K - $170K/yr

Full-time

Re-posted 26 days ago


Job description

With over 40 years of innovation, Quantum's end-to-end platform is uniquely equipped to orchestrate, protect, and enrich data across its lifecycle, providing enhanced intelligence and actionable insights. Leading organizations in cloud services, entertainment, government, research, education, transportation, and enterprise IT trust Quantum to bring their data to life, because data makes life better, safer, and smarter. Quantum is listed on Nasdaq (QMCO). For more information, visit www.quantum.com.

We are seeking a Senior Object Storage Software Engineer to design, implement, and optimize the object storage data path and core distributed services of our scale-out object storage platform. You will own software from the moment an object request hits the network interface through request processing, metadata operations, data placement, and durable persistence across the cluster.

This is a deep systems role for engineers passionate about low-latency code paths, high concurrency, and distributed systems correctness at scale.

Key Responsibilities
Object Storage Data Path

  • Design and optimize the object request pipeline for PUT/GET/DELETE and background operations, focusing on predictable latency, high throughput, and efficient CPU/memory usage in the critical path.
  • Build and maintain data path components such as request parsing/validation, routing, throttling, buffering, streaming I/O, and zero-copy / reduced-copy transfers where applicable.
  • Implement and tune distributed caching (read cache / metadata cache) and request coalescing strategies to reduce backend amplification and improve tail latency.
  • Identify and eliminate bottlenecks end-to-end (network → CPU → storage), leveraging deep Linux profiling and systems debugging skills.

Core Object Services & Distributed Architecture

  • Architect and evolve scale-out services for object metadata, namespace/indexing, placement, and cluster membership/state required for large clusters.
  • Design and maintain scalable, high-performance components such as metadata management and data placement algorithms across multi-node deployments.

Durability, Integrity & Resilience

  • Implement and maintain durability features such as erasure coding, replication, background healing, snapshots (where applicable), thin provisioning concepts, and data scrubbing to deliver “six nines” class durability.
  • Ensure correctness under failures: node loss, disk faults, partial writes, network partitions, and rolling upgrades—without compromising data integrity.

Concurrency, Locking & Correctness

  • Solve high-concurrency challenges in the object and metadata paths using robust synchronization strategies, lock minimization, and asynchronous execution models to maximize multi-core CPU utilization.
  • Apply careful correctness reasoning around ordering, idempotency, and race conditions in distributed request flows.

Scalability & Cluster Operations

  • Ensure the object data path and background systems scale linearly as nodes are added, including rebalancing and reconstruction workflows that minimize customer-visible impact.
  • Collaborate cross-functionally to integrate other platform components into the solution and operate effectively with remote teams.

Required Qualifications

  • 12+ years of software development experience using C/C++, Rust (or equivalent systems-level expertise).
  • Strong experience with distributed systems and high-performance storage software design.
  • Strong Linux experience, including debugging, profiling, and performance analysis of complex systems.
  • Solid understanding of concurrency, locking, and asynchronous programming models.
  • Strong communication skills; ability to collaborate in a team environment and across functions; ability to work effectively with remote teams.
  • Self-motivated, able to identify and solve problems independently, creative problem-solving mindset.
  • Willing and able to come to our office in Centennial, CO during core business hours (Tuesday - Thursday 10am-4pm).

Preferred / Nice-to-Have Skills

  • Experience with the Linux I/O subsystem and networking (plus).
  • Experience with storage protocols, clustering design and development (plus).
  • Experience with containers and Kubernetes (plus).
  • Experience using Git and Jira.
  • Experience with Amazon AWS S3 API, SDK’s & Tools.
  • Experience with AI tools or programming is a plus (especially for modern data pipelines and workload integration).

Quantum provides a diverse portfolio of health plans for medical and prescription, dental, vision, life, disability, and supplemental medical insurance options. We also support our team members’ efforts to develop and maintain a healthy lifestyle through reimbursement and educational programs. Quantum offers a company-matched 401(k) plan to help employees save for retirement in a tax-advantaged way. We also have an Employee Stock Purchase Program for purchasing Quantum stock at a discounted rate.

Anticipated Salary Range: $130,000 to $170,000 for qualified applicants.

The above pay range represents Quantum's good faith and reasonable estimate of possible compensation at the time of posting. Pay within the range will be based on a variety of factors, including but not limited to, relevant experience, knowledge/education, skills/abilities, internal equity, and budgetary considerations.

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