Senior Research Engineer

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Manhattan / $60 - $65 hour

INFO

Salary
SALARY:

$60 - $65

Location

LOCATION

Manhattan

Job Type
JOB TYPE

Contract

Responsibilities

  • Develop and deliver: Contribute across the full software development lifecycle, writing clean, tested, maintainable code.

  • Productionize ML solutions: Build and deploy LLM, NLP, and RAG applications, ensuring scalability, monitoring, and versioning.

  • Collaborate with researchers: Partner with research scientists to translate problem statements into deployable ML solutions.

  • Maintain quality and performance: Conduct code reviews, monitor models, prevent degradation, and ensure logging and documentation are in place.

  • Agile teamwork: Work in 2-week sprints with daily standups, collaborating with product managers, engineers, and scientists.

  • Communicate effectively: Break down complex technical concepts for both technical and non-technical audiences.

  • Experiment and innovate: Explore agentic workflows, prompt engineering, and emerging AI/ML approaches.


Required Skills & Experience

  • Bachelor's degree in Computer Science, related field, or equivalent experience.

  • 5+ years of software engineering experience, including:

    • 2+ years in ML product development.

    • 1+ year specifically in NLP tasks (NER, document analysis, information extraction, retrieval, classification).

  • Experience deploying ML/LLM solutions into production.

  • Strong proficiency in Python and modern development stacks.

  • Solid understanding of software engineering best practices, CI/CD, testing, DevOps, and SDLC.

  • Familiarity with cloud environments and scalable data pipelines.

  • Strong communication and collaboration skills; ability to work across teams of engineers and researchers.

  • Self-starter with proven ability to drive projects forward without micromanagement.


Preferred Skills & Experience

  • Hands-on experience with LLM frameworks (e.g., LangChain, LangGraph) and FastAPI.

  • Experience with RAG pipelines and agent-based workflows.

  • Knowledge of ModelOps/MLOps principles.

  • Familiarity with classical NLP as well as generative AI.

  • Additional programming languages (Java, Scala, Rust, Typescript, etc.) a plus.

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