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Literary Agent Reader Jobs (NOW HIRING)

Articul8

Applied AI Researcher (Dublin, CA)

Dublin, CA · On-site

... accelerate literature review, ablation studies, and mathematical analysis * Go broad: span ... You write code others can read, test, and extend. Fluent with Git and collaborative development ...

HR Green

Lead Electrical Engineer - Water/Wastewater

Mchenry, IL · On-site

$157K - $164K/yr

... agent of the company to sign engineering documents. * Possess strong technical verbal and technical writing skills and be able to read technical literature and engineering plans. * Possess strong ...

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HR Green

Lead Engineer

Saint Paul, MN · On-site

$104K - $166K/yr

... agent of the company to sign engineering documents. * Possess strong technical verbal and technical writing skills and be able to read technical literature and engineering plans. * Capable of ...

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HR Green

Lead Engineer - Water/Wastewater

Houston, TX · On-site

$76K - $104K/yr

... agent of the company to sign engineering documents. * Possess strong technical verbal and technical writing skills and be able to read technical literature and engineering plans. * Capable of ...

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HR Green

Lead Electrical Engineer - Water/Wastewater

Johnston, IA · On-site

$150K - $156K/yr

... agent of the company to sign engineering documents. * Possess strong technical verbal and technical writing skills and be able to read technical literature and engineering plans. * Possess strong ...

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HR Green

Lead Electrical Engineer - Water/Wastewater

Houston, TX · On-site

$155K - $161K/yr

... agent of the company to sign engineering documents. * Possess strong technical verbal and technical writing skills and be able to read technical literature and engineering plans. * Possess strong ...

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Timmons Group, Inc.

Senior Project Engineer

Richmond, VA · On-site

$100K - $140K/yr

... agent of the company to sign engineering documents. * Must be capable of correctly operating ... be able to read technical literature and engineering plans. * Must demonstrate two-way ...

Timmons Group

Senior Project Engineer

Ellicott City, MD · On-site

... agent of the company to sign engineering documents. * Must be capable of correctly operating ... be able to read technical literature and engineering plans. * Must demonstrate two-way ...

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Literary Agent Reader information

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How much do literary agent reader jobs pay per hour?

As of Jul 1, 2026, the average hourly pay for literary agent reader in the United States is $19.75, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $21.63 per hour, depending on experience, location, and employer.

What does a typical day look like for a Literary Agent Reader?

A typical day for a Literary Agent Reader involves reviewing submitted manuscripts, queries, or sample chapters to assess their suitability for representation. Readers write detailed reports or coverage, outlining the strengths, weaknesses, and market potential of each submission, and often meet with literary agents to discuss promising finds. While much of the work is independent and requires focused reading, collaboration with agents and occasional communication with authors or editors may also be part of the job. Many readers work remotely or on a freelance basis, managing their own schedules to meet submission deadlines. This role offers valuable exposure to the publishing industry and can serve as a stepping stone to more advanced positions within literary agencies.

What are the key skills and qualifications needed to thrive in the Literary Agent Reader position, and why are they important?

To thrive as a Literary Agent Reader, you need a strong background in literary analysis, excellent reading comprehension, and a keen understanding of market trends in publishing, often supported by a degree in English, literature, or a related field. Familiarity with manuscript tracking software, digital document management systems, and standard submission protocols is often required. Strong communication skills, professionalism, and the ability to provide constructive feedback help candidates excel in this position. These abilities are crucial because the role involves evaluating a high volume of submissions quickly and effectively, helping literary agents identify promising manuscripts and authors.

What is a Literary Agent Reader job?

A Literary Agent Reader evaluates manuscript submissions for a literary agency, assessing their quality, market potential, and fit for the agency. They provide written reports or coverage summarizing strengths, weaknesses, and suitability for representation. Readers help agents identify promising manuscripts but typically do not communicate directly with authors. This role is often freelance or entry-level, serving as a stepping stone to higher positions in publishing. Strong analytical skills and knowledge of market trends are essential for success.

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Literary Agent Reader jobs near you

Research Scientist, Agentic Data & Benchmarking

Institute of Foundation Models

Sunnyvale, CA • On-site

Full-time

Posted 23 days ago

Job description

About the Institute of Foundation Models
The Institute of Foundation Models (IFM) is a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.
As part of our team, you'll work at the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You'll help build groundbreaking AI systems with the potential to reshape entire industries, and contribute to establishing MBZUAI as a global hub for high-performance computing and deep learning.
About the role
The Agents team trains advanced agentic language models that use reasoning and tool use to complete real tasks on a computer. This is a specialist role at the center of the loop that drives those models: the data we train on and the benchmarks we measure against.
You'll own the agentic data pipeline end-to-end - sourcing and generating high-quality trajectories, tool-use data, and RL environments - and the evaluation suite that tells us, rigorously and reproducibly, what our agents can actually do. These two halves are inseparable: benchmarks expose where models fail, and targeted data closes the gap. The agents are only as good as the data they learn from and the evals that keep us honest, and this role owns both.
This is a research scientist position for someone who wants depth in data and measurement rather than breadth across the whole stack. You should be the kind of person who reads through datasets line by line, distrusts a metric until it's been validated, and gets satisfaction from making an eval suite that nobody questions.
Key responsibilities
Benchmarking & evaluation
  • Design and run evaluations of agentic capabilities - multi-step reasoning, tool use, long-horizon planning, computer use, and safety properties - turning ambiguous notions of "intelligence" into defensible, reproducible metrics.

  • Build and harden evaluation harnesses so benchmarks run reliably at scale against training checkpoints, with clear signal on regressions and model health.

  • Run experiments characterizing how prompting, sampling, scaffolding, and environment design affect agentic performance on internal and public benchmarks.

  • Diagnose anomalous eval results mid-training-run - determine whether the cause is the model, the data, the harness, or the infrastructure - and communicate the answer clearly.

Agentic data
  • Source, generate, and curate high-quality agentic training data: trajectories, tool-use traces, and task datasets for new capabilities.

  • Design and scale RL environments and reward signals, and measure their impact on model performance.

  • Manage technical relationships with external data vendors and domain experts, evaluating data quality and iterating quickly on feedback.

  • Develop QA frameworks that catch reward hacking, label noise, and contamination, keeping data and benchmark quality high.

Across both
  • Contribute to technical reports, research publications, and open-source benchmarks and tooling.

  • Partner with research and product teams to translate capability goals into measurable data and evaluation artifacts.

Qualifications
Academic qualifications
  • BS, MS, or PhD (or equivalent experience) in Computer Science, Machine Learning, or a related field.

Minimum qualifications
  • 2+ years of experience with a clear emphasis on evaluations and/or training-data curation for ML systems (related areas: LLM training/fine-tuning, RL, or distributed ML systems).

  • Strong Python and PyTorch development experience.

  • Demonstrated experience designing and deep-diving into evaluations, or curating and generating training datasets - ideally both.

  • Hands-on experience using LLM agents in your personal or professional work.

  • A habit of reading through raw data and trajectories to understand them and spot issues, and an instinct to distrust a metric until it's validated.

Preferred qualifications
  • Experience with reinforcement learning, reward design, or RL environment construction for LLMs.

  • Background in statistics and experimental design - a feel for signal-to-noise, statistical power, and contamination in evaluations.

  • Experience with large-scale dataset sourcing, curation, and processing, including working with external vendors or domain experts.

  • Strong knowledge of the literature on agent evaluation, RL, LLM reasoning, and tool use.

  • Experience building or operating data pipelines and evaluation infrastructure reliable at scale (e.g., PyTorch, Ray).

  • Experience evaluating or generating data for software-engineering or computer-use agents.

  • Contributions to published research, public benchmarks, and/or open-source ML software.

Representative projects
  • Stand up a new agentic benchmark from scratch - define the task, build the dataset and scoring, validate against known signals, and ship a view that makes the result legible to researchers and leadership.

  • Build an RL environment for a new high-value capability: design the reward, generate and QA the trajectory data, and measure the lift on model performance.

  • Diagnose a mid-training regression: an eval suite returns anomalous numbers and you determine whether it's the model, the harness, the data, or the infrastructure.

  • Partner with an external data vendor or domain expert to source high-quality trajectories, then build the QA framework that keeps reward hacking and contamination out.

  • Take a flaky distributed eval pipeline and make it reliable - better retries, better observability, faster feedback to researchers.

$150,000 - $450,000 a year
Salary Range
The posted salary range represents the company's good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.
We encourage you to apply even if you don't meet every qualification listed. Strong candidates rarely match every line, and we'd rather hear from you than have you rule yourself out.

Apply