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Data Annotation For Ai Jobs in Worcester, MA (NOW HIRING)

Buyers Edge Platform, LLC

Tableau Data Architect

Waltham, MA · Remote

$68.75 - $88.50/hr

Guide governance and quality standards for AI-generated content to ensure outputs align with the organization's trusted data standards. Architect and Design Tableau Data Models * Develop optimized ...

Intrepid

Artifical Intelligence (AI) Solution Architect

Lincoln, MA · On-site

$68.25 - $89.75/hr

  • Medical

  • Life

  • Retirement

Understanding of security, governance, and assurance considerations for AI, including data protection, model lifecycle management, and traceability of AI outputs * An active Secret clearance or the ...

Abbott

Staff AI/ML Engineer

Westford, MA

$99K - $198K/yr

Own end-to-end ML lifecycle, including data preparation, annotation strategies, model development ... Optimize models for real-time or near-real-time inference in embedded or product environments.

Abbott

Staff AI/ML Engineer

Westford, MA

$99K - $198K/yr

Own end-to-end ML lifecycle, including data preparation, annotation strategies, model development ... Optimize models for real-time or near-real-time inference in embedded or product environments.

Abbott

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Own end-to-end ML lifecycle, including data preparation, annotation strategies, model development ... Optimize models for real-time or near-real-time inference in embedded or product environments.

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All JobsData Annotation For Ai JobsData Annotation For Ai Jobs in Worcester, MA

Data Annotation For Ai information

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

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Data Annotation For Ai jobs near you

Director, Engineering - AI Platform (Agentic AI)

Staples, Inc.

Framingham, MA

$260K/yr

Full-time

Retirement, PTO

Re-posted 10 days ago

Staples rating

5.8

Company rating: 5.8 out of 10

Based on 633 frontline employees who took The Breakroom Quiz

426th of 733 rated retailers

Job description

Our digital solutions team is more than a traditional IT organization. We are a team of passionate, collaborative, agile, inventive, customer-centric, results-oriented problem solvers. We are intellectually curious, love advancements in technology and seek to adapt technologies to drive Staples forward. We anticipate the needs of our customers and business partners and deliver reliable, customer-centric technology services.

The Director of Engineering, Agentic AI is responsible for defining and delivering the enterprise strategy for AI-enabled software engineering, with a focus on building a secure, scalable, and production-grade agentic AI platform across the software development lifecycle (SDLC). This role leads the transformation of engineering through AI-driven tools, workflows, and operating models that improve developer productivity, software quality, and speed to market.

This leader owns the end-to-end AI developer experience, including platform strategy, ecosystem integration, governance, and measurable outcomes. The role partners cross-functionally with Security, Risk, Legal, Infrastructure, and Product teams to enable responsible and scalable adoption of AI capabilities across the enterprise.

Staples is at an inflection point in applying AI to the software development lifecycle. While we have successfully deployed AI across customer-facing and enterprise functions, we are in the early stages of transforming how software is built, tested, and delivered. This role is critical to establishing a scalable, cost-efficient, and enterprise-grade AI engineering ecosystem.

What you'll be doing: 

  • Drive end-to-end transformation of the SDLC, ensuring AI is embedded across requirements, development, testing, deployment, and post-release observability-not just code generation.

  • Design and implement secure, compliant AI platforms with embedded governance, guardrails, and auditability.
  • Establish and scale AI-powered capabilities including code assistants, agentic workflows, test automation, and developer productivity tools.
  • Build and integrate a developer productivity ecosystem spanning collaboration tools, workflows, knowledge systems, and AI platforms.

  • Define and track outcome-based metrics for developer productivity, software quality, and operational effectiveness, leveraging telemetry, observability frameworks, and reporting to measure AI impact at scale.

  • Ensure scalability, reliability, resiliency, and cost optimization of AI and distributed systems.
  • Evolve engineering operating models, delivery practices, and standards to support AI-enabled development.
  • Partner with cross-functional stakeholders (Security, Risk, Legal, Infrastructure, Product) to drive safe and compliant AI adoption.
  • Advise executive leadership on emerging AI trends, risks, and enterprise opportunities.
  • Lead vendor evaluation, selection, negotiations, and ongoing management for AI platforms and tools.

  • Lead developer adoption, training, and governance frameworks to ensure responsible, effective use of AI across engineering teams.

  • Drive continuous improvement in engineering processes, quality standards, and platform capabilities.

  • Define and optimize model usage strategies across use cases, balancing performance, cost, and scalability (e.g., token consumption, model selection, and workload segmentation).

  • Evaluate and select AI tools, models, and platforms in a rapidly evolving landscape, aligning solutions to use case, cost, and performance requirements.

What you bring to the table: 

  • Proven experience implementing AI-enabled software development lifecycle transformation in production environments, including end-to-end integration and scaling across multiple engineering teams.
  • Strategic thinking with the ability to translate vision into execution
  • Strong leadership and team-building capabilities
  • Influencing and stakeholder management skills across all organizational levels
  • Advanced problem-solving and critical thinking abilities
  • Adaptability in a fast-changing, emerging technology landscape
  • Results orientation with a focus on measurable outcomes
  • Strong communication and storytelling skills for executive audiences
  • Collaborative mindset with a focus on cross-functional partnership

What's needed- Basic Qualifications: 

  • Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field or equivalent work experience.
  • 10+ years designing and delivering distributed systems in production environments.
  • 5+  years leading managers and multi-level engineering teams.
  • 2+ years building and scaling AI/ML or agentic systems, including multi-agent workflows and model lifecycle management.
  • Experience with at least one enterprise AI platform (e.g., Azure AI, AWS Bedrock, Google Gemini, Databricks).
  • Experience implementing agentic AI capabilities (e.g., orchestration, tool use, memory, evaluation).
  • Experience building scalable, reliable, and cost-efficient distributed systems.
  • Proficiency in one or more programming languages (Java, Python, TypeScript, or similar).
  • Demonstrated experience leading engineering teams, including managing managers and developing talent.
  • Strong cross-functional leadership and stakeholder influence skills.

  • Hands-on experience designing and deploying AI-enabled engineering platforms-not solely defining strategy.

  • Ability to translate complex technical concepts into executive-level insights.

What's needed- Preferred Qualifications: 

  • Master's degree in Computer Science, AI, Data Science, or related field
  • Experience with RAG, GraphRAG, vector databases, and enterprise knowledge integration patterns
  • Experience with AI orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex)
  • Experience implementing AI governance, responsible AI, and model risk management frameworks
  • Experience building secure AI platforms (e.g., access controls, audit logging, secrets management)
  • Experience defining and tracking engineering productivity or quality metrics tied to business outcomes
  • Experience leading enterprise-scale technology or platform transformations
  • Experience managing vendor selection, negotiations, and partnerships

We Offer:

  • Inclusive culture with associate-led Business Resource Groups
  • 22 days of PTO and Holiday Schedule (7 observed paid holidays + 1 floating holiday)
  • Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!

The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.

Staples is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, protected veteran status, disability, or any other basis protected by federal, state, or local law.

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