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Gpt Researcher Jobs (NOW HIRING)

Spark Tek Inc

AI Engineet

San Jose, CA · On-site

$134K - $161K/yr

The role involves researching and prototyping advanced RAG techniques, implementing Agentic AI ... GPT-4+, Gemini, LLaMA, and others for various use cases. • Apply design patterns in AI and ...

Haize Labs

Research Intern

New York, NY · On-site

$125K - $200K/yr

What sets us apart We are not here to write GPT wrappers or get rich quick off the AI bubble. We're here to work on the hardest, most fundamental research problem in AI: making it reliable and robust.

Haize Labs

Research Intern

Manhattan, NY · On-site

$125K - $200K/yr

What Sets Us Apart We are not here to write GPT wrappers or get rich quick off the AI bubble. We're here to work on the hardest, most fundamental research problem in AI: making it reliable and robust.

Haize Labs

Research Intern

New York, NY · On-site

$125K - $200K/yr

What sets us apart We are not here to write GPT wrappers or get rich quick off the AI bubble. We're here to work on the hardest, most fundamental research problem in AI: making it reliable and robust.

Saransh Inc

Gen AI Tech Lead

Westlake, TX · On-site

... GPT, LLaMA, Claude). * Lead the evaluation and integration of foundation models and APIs (e.g ... Knowledge of prompt engineering, fine-tuning, and model optimization techniques Innovation & R&D: * ...

CYNET SYSTEMS

Project Manager III

Morristown, NJ · On-site

$82 - $87/hr

At least 10+ years experience in life science and successful R&D track record for evidence ... Represent P&HV department at the GPT level. * From M2-Ph3 develop the patient-informed TVP and ...

Xforia, Inc.

Staff ML Engineer

San Jose, CA · On-site

... GPT, BERT, and their variants, and a track record of applying these models to real-world ... research papers, techniques, and advancements in deep learning and related fields • Strong ...

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Gpt Researcher information

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How much do gpt researcher jobs pay per year?

As of Sep 11, 2026, the average yearly pay for gpt researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Gpt Researcher job openings in the United States as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, 1% Temporary, and 5% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.

Machine Learning Engineer - LLMs and Agentic

GA • Remote

Oversight Systems Inc
See more jobs at Oversight Systems Incexternal

Full-time

Posted 15 days ago

Job description

About Oversight

Oversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.

Position Overview:

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.

The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.

Core ML/LLM Engineering
  • Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.
  • Implement RAG pipelines using vector databases.
  • Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.
  • Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
  • Integrate memory, evidence packs, and explainability modules into agentic pipelines.
  • Work hands-on with multiple LLM ecosystems:
    • OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).
    • Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).
    • Google Gemini (multimodal reasoning, advanced RAG integration).
    • Meta LLaMA (fine-tuned/custom models for domain-specific tasks).
Data & Infrastructure
  • Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
  • Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
  • Implement model monitoring, drift detection, and retraining pipelines.
  • Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
Research & Applied Innovation
  • Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
  • Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
  • Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
  • Translate research prototypes into production-ready components.
Collaboration & Delivery
  • Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
  • Participate in design reviews, architecture discussions, and model evaluations.
  • Document processes, experiments, and results effectively for knowledge sharing.
  • Mentor junior engineers and contribute to ML engineering best practices.


Education, Experience and Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
  • 3+ years of experience building and deploying ML systems.
  • Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
  • Demonstrated experience with at least two of the following ecosystems:
    1. OpenAI GPT models (chat, assistants, fine-tuning).
    2. Anthropic Claude (safety-first AI for reasoning and summarization).
    3. Google Gemini (multimodal reasoning, enterprise-scale APIs).
    4. Meta LLaMA (open-source, fine-tuned models).
  • Familiarity with vector databases, embeddings, and RAG pipelines.
  • Ability to work with structured and unstructured data at scale.
  • Knowledge of SQL and distributed data frameworks (Spark, Ray).
  • Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
Preferred Qualifications
  • Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
  • Knowledge of AI safety, guardrails, and explainability techniques.
  • Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
  • Experience with CI/CD for ML (MLOps), monitoring, and observability.
  • Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
  • Contributions to open-source AI/ML projects or publications in applied ML research.