About Meitner Energy

Meitner Energy is developing advanced nuclear energy solutions intended to deliver reliable, scalable, and carbon-free electricity for industrial and grid applications.

Our international team brings together nuclear, engineering, commercial, regulatory, and project-development experience. We are building an organization focused on disciplined engineering, responsible execution, and the deployment of nuclear energy at meaningful scale.

The Opportunity

Meitner’s AI platform is only as useful as the retrieval layer beneath it, and this role owns that layer end to end. You will build the ingestion pipeline that processes a large corpus of regulatory and engineering documents, implement the hybrid retrieval and reranking stack that surfaces the right passage from the right document, and maintain the evaluation set that tells you whether retrieval is actually improving. The corpus includes scanned engineering drawings, technical standards, and regulatory material across multiple jurisdictions, so the parsing and optical character recognition problems are substantial and real.

Because the corpus contains export-controlled and otherwise sensitive material, every retrieval decision carries an access-control dimension. Access enforcement happens before retrieval rather than as a filter applied afterward, and citations must trace back to the source document and page. You will own the retrieval quality metric, the evaluation set behind it, and the pipeline that keeps both current as the corpus and the models change.

This role is adecuado for someone who:

- Treats retrieval quality as a first-class engineering problem rather than a setting to configure once and forget.
- Has worked with messy real-world document corpora, including scanned files, mixed formats, and inconsistent metadata, and knows what making them searchable actually costs.
- Builds evaluation into the work rather than adding it after someone complains about a bad answer.
- Is energized by combining data engineering precision with high-consequence nuclear regulatory and engineering content.

What You'll Do

Document Ingestion and Processing

Build and maintain the end-to-end ingestion pipeline for the regulatory and engineering document corpus, covering text extraction, optical character recognition of scanned material, chunking strategy, and structured metadata generation. You will handle document types that resist naive parsing, including engineering drawings, dense tables, and figures, developing extraction approaches that preserve the structural information retrieval depends on. Access-control labeling is applied and maintained at the document and chunk level during ingestion, so that classification metadata is accurate and auditable before any content reaches the index.

Retrieval and Reranking

Implement hybrid retrieval that combines dense vector search with sparse keyword search across a production search and vector retrieval stack,



and tune the blend for the specific characteristics of an engineering and regulatory corpus where exact identifiers matter as much as semantic similarity. You will integrate and tune reranking models to improve precision on the results returned to the model, weigh the latency cost of doing so, enforce access filtering before retrieval rather than after, and implement citation generation that traces every answer to a source document, section, and page.

Evaluation and Quality

Build and maintain a gold retrieval evaluation set that represents the queries the platform will genuinely face, and own the metrics and the cadence on which they are run. You will implement extraction quality checks that detect parsing failures, character recognition errors, truncated chunks, and metadata gaps before they propagate into the index, and contribute retrieval quality data to the platform evaluation harness so that regressions introduced by model or pipeline changes are visible early.

What We're Looking For

Required Qualifications

- Bachelor’s degree in computer science, engineering, or a related technical discipline, or equivalent professional experience.
- 3+ years of professional experience in data engineering, machine learning engineering, or a closely related role, with a focus on document processing and retrieval systems.
- Strong Python, with demonstrated experience building and operating production data pipelines.
- Hands-on experience with production vector databases and with embedding model deployment.
- Practical experience with optical character recognition tooling and document-parsing pipelines for complex document collections, including familiarity with their failure modes.
- Working knowledge of retrieval quality evaluation methodology, hybrid retrieval, and reranking models.
- Understanding of why access enforcement must precede retrieval in a regulated environment, and the ability to implement document-level access control in a vector search context.
- Ability to work on-site in Dallas.

Preferred Qualifications

- Working proficiency in Spanish. Spanish is preferred because the corpus and the collaborating teams span English and Spanish material.
- Experience with engineering or regulatory document collections such as technical standards, drawings, and compliance filings.
- Familiarity with access-controlled retrieval in a regulated or security-sensitive environment.
- Exposure to prompt engineering and an understanding of how retrieval quality affects generated output.
- Background in nuclear energy, defense, aerospace, or another regulated industrial sector.

The Candidate We Are Seeking The strongest candidate will be an engineer who has made a difficult corpus genuinely searchable and can prove it with numbers.



This may be an excellent next step for a data engineer, search engineer, or machine learning engineer who wants full ownership of a retrieval layer rather than a slice of one. Candidates should be prepared to discuss pipelines they personally built, the specific ways their source documents defeated their first approach, and how they measured the improvement.

Why Join Meitner Energy?

Consequential Work. Build the retrieval layer that makes decades of nuclear engineering and regulatory material usable to the people delivering carbon-free energy.

Direct Ownership. Own the pipeline, the metric, and the evaluation set outright rather than tuning parameters on someone else’s system. Meitner is small enough that your decisions visibly shape the company.

International Scope. Work with professionals across the United States, Argentina, and the United Kingdom.

Strong Benefits. Meitner offers comprehensive health insurance, a 401(k) retirement plan, and participation in the company's employee stock option program, subject to plan terms and eligibility. The final offer will reflect the candidate's depth and relevant skills, including retrieval depth, document processing experience, Spanish proficiency, and regulated-industry background.

High-Quality Workplace. Work from a modern Dallas office designed to support collaboration, productivity, and employee well-being, including an on-site fitness facility.

To Apply

Email your resume and a written response of no more than one page to [email protected]. Use this exact subject line: Data / RAG Engineer / your full name / hands-on. Applications without it will not be reviewed.

Write the response yourself, in your own words, for this posting specifically. Answer directly with specific examples; generic statements of engineering philosophy or best practices are not considered responsive, and responses that appear mass-produced or machine-generated are declined without review. Your one page should address the following:

- Quote the single sentence from this posting that best describes how this role differs from your current position, and explain why that difference appeals to you.
- Describe the most complex AI or LLM platform system you have designed, built, or operated in the past three years. What was the stack, what were the hardest trade-offs, what broke, and how did you fix it?
- Walk us through how you would fit a large open-weight model onto a fixed GPU memory budget. What trade-offs would you consider, and how would you verify the result?
- Describe how you have approached data-sensitivity routing or access control in a regulated or security-sensitive environment. What did you build, and how did you verify it held?
- Explain why this role is the right next step in your career, and identify one aspect of the position that may be more demanding than your current role.

Please do not include confidential, proprietary, export-controlled, or otherwise restricted information belonging to a current or former employer.

📌 Data / RAG Engineer (Provincia de Buenos Aires)
🏢 Meitner Energy
📍 Provincia de Buenos Aires