# Software Engineer

**Company:** [Abridge](https://hotfix.jobs/companies/abridge)
**Location:** San Francisco, CA, New York, NY
**Role:** ML Engineering
**Salary:** $157k – $184k/yr
**Experience:** 0+ years
**Skills:** LLMs, RAG, LangChain, Llamaindex, Python, JavaScript, Agentic Systems, Prompt Engineering, Evaluation Frameworks, Retrieval Pipelines
**Posted:** 2026-07-13

> Early-career Software Engineer building and iterating on agentic LLM systems, RAG pipelines, evaluation frameworks, and backend/frontend components for AI-powered clinical documentation at Abridge. Requires CS degree or equivalent plus hands-on GenAI experience from projects, internships or coursework.

## Job Description

## What You'll Do
- Build and iterate on agentic LLM systems — including retrieval pipelines, structured tool use, and chained LLM workflows — as part of a collaborative engineering team
- Contribute to evaluation frameworks that measure accuracy, robustness, and clinical reliability — including automated pipelines and human-in-the-loop review
- Contribute to backend and frontend systems across Abridge
- Think with an agent-first approach. Continuously learn and relearn agentic coding.
- Fine-tune your judgment on where the human in the loop is critical.
- Help prototype with new models, prompting techniques, and open-source orchestration tools (LangChain, LlamaIndex, etc.)
- Contribute to monitoring and observability systems that keep our LLM workflows healthy in production
- Collaborate across ML, infrastructure, product, and clinical teams — and develop a genuine understanding of the users and clinicians we serve
- Learn from senior engineers and bring curiosity and fresh perspective to every problem

## What You'll Bring
- A degree in CS or a related field, or equivalent experience (projects, bootcamp, open source — we care about what you can build not where you learned it)
- Hands-on GenAI experience — through coursework, personal projects, internships, or hackathons. You've integrated an LLM API, built a RAG pipeline, experimented with agents, or shipped something AI-powered and can speak to what you learned
- Familiarity with LLM orchestration concepts (prompt chaining, tool use, retrieval) even if you haven't yet used them in a production setting
- AI tooling is already part of how you work — you use it to write, debug, and learn faster, and you know when to trust it vs. verify
- Curiosity about model evaluation, failure modes, and what it actually takes to make an LLM system reliable
- Collaborative, low-ego mindset — we move fast, and we need teammates who pitch in wherever needed

## Compensation and Benefits
- Competitive compensation and equity grants for full time employees
- Generous Time Off: 14 paid holidays, flexible PTO for salaried employees
- Comprehensive Health Plans: Medical, Dental, and Vision coverage
- Generous HSA Contribution
- Paid Parental Leave
- Family Forming Benefits
- 401(k) Matching
- Personal Device Allowance
- Pre-tax Benefits: FSA and Commuter Benefits
- Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more
- Mental Health Support: therapy and coaching
- Sabbatical Leave after 5 years

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