Junior Software Engineer (Backend + AI)

Newton Research
Visa Sponsorship
$90K - $110K/yr

AI Infrastructure

Agentic Frameworks

Tech Stack

About the Role

Newton Research is hiring a Junior Software Engineer (Backend + AI) for our AI-powered research and analysis platform used by enterprises to unlock insights from their data.

Position: Permanent, Full-time, Junior
Format: Hybrid (Greater Boston Area)
Compensation: $90,000 - $110,000

What you'll do:

  • Build Django REST Framework API endpoints with complex data modeling.
  • Extend AI agent capabilities — Add new tools to our LangGraph-based agents. Understand how retrieval-augmented generation works by working on our memory system.
  • Write async task workers for document parsing and LLM inference.
  • Implement comprehensive test coverage with pytest fixtures and API mocking.
  • Build React components with TypeScript.
  • Diagnose and fix AI output issues ("debug AI output").

You'll work on AI agent pipelines that call LLMs, execute tools, and reason over enterprise data.

Tech Stack:

  • Backend: Python 3.13, Django 5.2, Django REST Framework, PostgreSQL, Redis
  • AI/ML & Orchestration: OpenAI, Anthropic, and Google LLM APIs; LangChain + LangGraph agent orchestration; sentence-transformers for vector embeddings; RAGAS evaluation
  • Data Science: NumPy, Pandas, Polars, scikit-learn, XGBoost, PyMC, Prophet, Plotly
  • Frontend: React 19, TypeScript, Vite, Ant Design, TanStack Query, SCSS Modules
  • Infrastructure: Docker, GitHub Actions, AWS (S3, ECR), MinIO, Sentry, RQ (Redis Queue)
  • Testing: pytest (4,700+ tests), Vitest, Playwright E2E

Required Qualifications:

  • Solid Python fundamentals and debugging ability.
  • Web API knowledge (HTTP, JSON, request/response cycles).
  • Git proficiency.
  • Database experience (SQL, schema design).
  • Genuine curiosity about AI/ML — you've used LLM APIs, built a RAG pipeline, fine-tuned a model.
  • Ability to debug AI-generated code.

Nice-to-Have Skills:

  • Django or Flask experience.
  • React/TypeScript exposure.
  • Docker familiarity.
  • Vector databases, embeddings, LLM orchestration frameworks.
  • Open-source contributions.
  • Deployed projects.

Application Requirements:

  1. GitHub or portfolio (deployed project, open-source, or documented experiment).
  2. A note on what you've built with AI tools (emphasis on building, not just using).
  3. Resume.

Visa sponsorship may be available — application form asks about future sponsorship needs.

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