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Llm Annotation Jobs in Baltimore, MD (NOW HIRING)

Omm IT Solutions

Senior Data Scientist

Ellicott City, MD · On-site

Train and optimize NLP\/LLM models and create Python based pipelines \n * Experience building cloud ... annotation tools and semantic frameworks. \n * Ability to clean and process large amounts of real ...

vetegrity

Data Scientist 3

Annapolis Junction, MD · On-site

Roles may include supporting NLP, LLM, and agentic AI initiatives, as well as optimizing data flows ... annotation, model training/validation, and knowledge extraction from unstructured data ...

All JobsLlm Annotation JobsLlm Annotation Jobs in Baltimore, MD

Llm Annotation information

See Baltimore, MD salary details

$10.9K

$41.2K

How much do llm annotation jobs pay per year?

As of Aug 17, 2026, the average yearly pay for llm annotation in Baltimore, MD is $39,745.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,700.00 and $39,700.00 per year, depending on experience, location, and employer.

What is LLM annotation?

LLM annotation refers to the process of labeling or tagging data specifically for training and evaluating large language models (LLMs) like GPT or BERT. Annotators read text and apply labels, correct errors, or provide feedback to help improve the model's understanding and performance. This work is crucial for supervised learning, as well-annotated datasets help LLMs better recognize patterns, context, and meaning in human language. LLM annotation can involve tasks such as sentiment analysis, named entity recognition, or instruction following. Annotators often use specialized platforms or tools to complete their tasks efficiently and accurately.

What are the key skills and qualifications needed to thrive as an LLM annotation specialist?

To thrive as an LLM Annotation Specialist, you need strong analytical skills, attention to detail, and a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, natural language processing (NLP) tools, and data labeling systems is typically required. Excellent communication, critical thinking, and the ability to follow guidelines precisely are valuable soft skills for this role. These skills ensure high-quality, accurate data annotation, which directly impacts the performance and reliability of large language models.

What are some common challenges faced by LLM annotation specialists, and how can they be addressed?

LLM Annotation specialists often encounter challenges such as interpreting ambiguous language data, maintaining annotation consistency across complex datasets, and keeping up with evolving guidelines. These can be addressed by participating in regular team syncs to clarify guidelines, using annotation tools with built-in quality checks, and collaborating closely with project leads and fellow annotators. Continuous learning and open communication help ensure high-quality, reliable data annotation and support professional growth within the AI and NLP fields.

What is the difference between Llm Annotation vs Data Labeler?

AspectLlm AnnotationData Labeler
Required CredentialsBasic computer skills, sometimes familiarity with AI toolsBasic skills, often on-the-job training
Work EnvironmentRemote or office-based, tech-focusedRemote or on-site, varied industries
Industry UsageAI, machine learning, NLP projectsVarious industries including marketing, healthcare, and tech
Search & Comparison IntentUnderstanding roles in AI data preparationGeneral data labeling tasks

In summary, Llm Annotation involves specialized annotation for large language models, often requiring familiarity with AI tools, while Data Labeler is a broader role focused on labeling data across multiple industries with minimal technical requirements.

How to become an Llm annotator?

To become an LLM annotator, candidates typically need strong language skills, attention to detail, and familiarity with data annotation tools. Many positions require a high school diploma or equivalent, and some may prefer prior experience in data labeling or related fields. Training is often provided by employers to ensure accurate annotation of large language model datasets.

What are popular job titles related to Llm Annotation jobs in Baltimore, MD?

For Llm Annotation jobs in Baltimore, MD, the most frequently searched job titles are:

What job categories do people searching Llm Annotation jobs in Baltimore, MD look for?

The top searched job categories for Llm Annotation jobs in Baltimore, MD are:

Llm Annotation jobs near you

Principal Firmware Reverse Engineer (AI-Assisted)

Bigbear.ai

Columbia, MD

$151K - $227K/yr

Full-time

Re-posted 4 days ago

Job description

Overview

BigBear.ai is seeking a Principal Firmware Reverse Engineer focused on advancing reverse engineering capabilities through AI-assisted tooling and automation. You will design and implement tooling enhancements that empower reverse engineers to work more efficiently on firmware, embedded systems, and application software. This role bridges deep reverse engineering expertise with emerging AI/LLM technologies to create next-generation analysis workflows.


Responsibilities

  • Design and implement tooling enhancements to support reverse engineering workflows for firmware, embedded systems, and application software
  • Experiment with autonomous or semi-autonomous agents to assist with development and analysis workflows
  • Extend or integrate reverse engineering tools such as Ghidra to enhance team capabilities
  • Develop automation for common reverse engineering tasks, including analysis and documentation
  • Apply AI-assisted techniques to binary analysis, annotation, and pattern identification
  • Collaborate with customers and teammates to gather feedback and iterate on solutions
  • Measure and document the effectiveness of tooling improvements
  • Provide technical leadership and mentorship to junior engineers on the team

Qualifications

  • 12+ years of relevant experience, or Bachelor's degree in a technical discipline + 4+ years of experience
  • Clearance:TS/SCI w/Poly
  • Hands-on experience with reverse engineering tools such as Ghidra or IDA Pro
  • Strong understanding of low-level software concepts (assembly, binaries, memory, calling conventions)
  • Experience developing software tools, scripts, or plugins to support analysis workflows
  • Proficiency in at least one scripting or programming language commonly used in reverse engineering workflows
  • Ability to prototype, evaluate, and refine experimental tooling
  • AI/LLM curiosity and readiness to learn on-the-job; familiarity with AI/LLM technologies and interest in advancing your skills to the next level