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Entry Level Large Language Model Llm Jobs in Massachusetts

Nastech Global

GCP Gen AI Architect

Boston, MA · On-site

Architect GEN AI solutions using GCP (GEN AI LLM, Big Query, Vertex AI and related services. Fine-tune pretrained large language models (LLMs) and other generative models tailored to specific domain ...

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Entry Level Large Language Model Llm information

What is an entry level large language model LLM?

An Entry Level Large Language Model (LLM) role typically refers to positions where individuals work with advanced AI systems, like ChatGPT or similar models, to support tasks such as data annotation, model evaluation, prompt engineering, or customer support. Entry-level LLM professionals might help train models, test outputs for accuracy, or assist with basic research. These roles usually require strong analytical skills, attention to detail, and some familiarity with AI concepts, but do not always require advanced programming experience. They offer a great starting point for those interested in the field of artificial intelligence and natural language processing.

What types of projects do entry level large language model LLM engineers typically contribute to?

Entry-level professionals in LLM roles often support data preparation, model fine-tuning, and evaluation tasks under the guidance of more experienced engineers or data scientists. They may annotate data, help run experiments, monitor model outputs for quality, and assist in deploying models for internal testing or limited production use. Collaboration with cross-functional teams—including machine learning engineers, product managers, and research scientists—is common, offering valuable exposure to various stages of the LLM development lifecycle. This hands-on experience helps build foundational skills and prepares individuals for more advanced responsibilities in the field.

What are the key skills and qualifications needed to thrive as an entry level large language model LLM engineer?

To thrive as an Entry Level Large Language Model (LLM) Engineer, you need a solid background in computer science, machine learning fundamentals, and proficiency in programming languages like Python, typically supported by a relevant degree. Familiarity with machine learning frameworks (such as PyTorch or TensorFlow), version control systems, and cloud computing platforms is often required. Strong analytical thinking, problem-solving skills, and effective communication set candidates apart in this role. These competencies are crucial for developing, fine-tuning, and deploying LLMs to ensure innovative and reliable AI solutions.

What is the difference between Entry Level Large Language Model Llm vs Data Analyst?

AspectEntry Level Large Language Model LlmData Analyst
Required CredentialsBasic understanding of NLP, programming skills (Python), coursework or certifications in AI/MLBachelor's degree in Data Science, Statistics, or related field; often certifications in data analysis tools
Work EnvironmentResearch labs, AI companies, tech startups; focus on model development and trainingBusiness environments, consulting firms, finance, healthcare; focus on data interpretation and reporting
Industry UsageAI development, NLP applications, machine learning researchBusiness intelligence, market analysis, operational insights

Entry Level Large Language Model Llm roles focus on developing and training NLP models, requiring programming and AI knowledge. Data Analysts interpret data to inform business decisions, often using statistical tools. While both roles involve working with data, Llm positions are more technical and research-oriented, whereas Data Analysts focus on data interpretation and reporting.

What are the most commonly searched types of Large Language Model Llm jobs in Massachusetts?

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Entry Level Large Language Model Llm jobs near you
Infographic showing various Entry Level Large Language Model Llm job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 90% Full Time, 6% Part Time, and 3% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution.

Machine Learning Engineer (LLM) (Boston)

DeepRec.ai

Boston, MA • On-site

$170K - $200K/yr

Full-time

Re-posted 23 days ago

Job description

Machine Learning Engineer (LLM)

Compensation: $170,000 - $200,000+ (DOE)
Location: Boston or Berkeley, flexible 2-3 days per week in office

We’re working a fast‑growing AI company on a mission to automate complex workflows in the financial services sector, starting with insurance. Their technology leverages cutting‑edge AI to simplify high‑value processes, from multi‑turn conversations to full workflow automation.

As an ML Engineer within LLMs, you’ll be building and scaling advanced AI systems that power intelligent, multi‑agent workflows. You’ll take ownership of designing, fine‑tuning, and productionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, DevOps, and leadership to shape the AI infrastructure that drives the company’s automation solutions.

What You’ll Do
  • Build, fine‑tune, and productionize large language model (LLM) pipelines, including PEFT, RLHF, and DPO workflows.
  • Develop APIs, data pipelines, and orchestration systems for multi‑agent, multi‑turn AI conversations.
  • Integrate models with backend services, including voice orchestration platforms and transcript generation.
  • Optimize model usage and efficiency, transitioning from external APIs to in‑house solutions.
  • Collaborate cross‑functionally with data scientists, DevOps, and leadership to deliver scalable machine learning solutions.
What We’re Looking For Essential Skills & Experience
  • Strong proficiency in Python and ML frameworks (e.g., scikit‑learn, TensorFlow, PyTorch).
  • Hands‑on experience fine‑tuning and training LLMs.
  • Experience with PEFT, DPO, Prefence Optimization, post‑training, supervised fine tuning, RLHF.
  • Familiarity with AWS suite and deploying ML models to production.
  • Ability to reason deeply about ML principles, architectures, and design choices.
  • Knowledge of multi‑agent orchestration and conversational AI systems.
Desirable Skills & Experience
  • Background in voice AI, speech‑to‑text, or text‑to‑speech systems.
  • Exposure to financial services or insurance applications.
  • Familiarity with optimizing models for long‑context scenarios.

For additional information or to apply, please get in touch or apply directly.

Seniority level

Not Applicable

Employment type

Full‑time

Job function

Information Technology

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