Software Engineer, Abridge
GenAI
$255-300k
+ Equity
More information about location
3 days a week in office
Powering deeper understanding in healthcare through purpose-built AI
Be an early applicant
Powering deeper understanding in healthcare through purpose-built AI
101-200 employees
Be an early applicant
$255-300k
+ Equity
More information about location
3 days a week in office
101-200 employees
Company mission
To power a deeper understanding in healthcare.
Role
Who you are
- We are looking for passionate GenAI Engineers of all levels who are passionate about making a positive impact
- 3+ years of experience building production-grade systems, with 1–2+ years focused on GenAI or LLM-powered products
- Deep fluency with LLM APIs, prompting strategies, and orchestration patterns (e.g., LangChain, LlamaIndex, custom pipelines)
- Experience with retrieval systems (e.g., semantic and lexical retrieval, vector DBs, efficient kNN), function calling, tool-use, or agentic workflows
- Working knowledge of model evaluation, experience building diverse datasets, conducting both automated and human-in-the-loop evaluations, running A/B tests, and working with subject matter experts to guide model improvement
- Strong Python fundamentals—including ability to write clean code, design comprehensive test-cases, and familiarity with core language features and standard libraries; experience with async programming, performance profiling, packaging, and deployment tooling is strongly preferred
- Good taste and intuition: You know when to move fast, ship, and iterate and also when to take a beat to tackle tech debt
- We value people who are eager to learn new things and recognize that great team members might not perfectly match a job description. If you’re interested in the role but aren’t sure whether or not you’re a good fit, we’d still like to hear from you
What the job involves
- You’ll collaborate closely with a cross-functional team of researchers, clinicians, and engineers to translate cutting-edge language model capabilities into dependable, real-world clinical systems
- Your focus will be on designing advanced LLM-driven workflows that can reason through complex clinical contexts, leverage agentic capabilities and structured tool use, navigate branching chains of LLM calls, integrate seamlessly with retrieval systems, and consistently generate outputs that meet the highest standards of clinical reliability and trust
- A major part of this role will involve developing and applying rigorous evaluation frameworks (both automated and human-in-the-loop) to continuously assess accuracy, robustness, multilingual capabilities, and more
- This is an opportunity to design experiments to probe failure modes, simulate edge cases, and stress-test LLM workflows under realistic load and challenging real-world conditions
- You’ll apply a disciplined, data-driven approach to understanding model behavior—developing tools to measure system performance, conduct A/B tests against established baselines, and generate clear, actionable insights that inform deployment decisions
- This high impact role will own the end-to-end productionization of LLM workflows: deploying models into low-latency, high-uptime environments, building monitoring and observability systems, implementing post-processing guardrails, and managing workflow versioning
- You'll design and build GenAI systems that turn LLMs into composable, dependable tools—leveraging retrieval, tool use, agentic reasoning, and structured outputs
- Collaborate with ML and infra engineers to scale and optimize GenAI workflows, managing latency, context windows, and model choice
- Write high-quality, modular code that’s graceful under failure, flexible to change, and easy to iterate on
- Own major architectural decisions—how we architect workflows,define data flow, cache intermediate state, and structure generative outputs
- Drive rigorous evaluation: build benchmark datasets, develop automated and human-in-the-loop frameworks, design experiments to surface failure modes and edge cases, run A/B tests to inform deployment, and distill insights from clinician feedback to evaluate and guide model improvement
- Leverage frontier capabilities: rapidly prototype with new models and model capabilities, open-source tools, and novel prompting techniques
Our take
Summarising key elements of healthcare conversations is crucial for both physicians and patients. Medical scribes, though effective, are costly, and the healthcare system faces funding and staffing crises. This has created a demand for more efficient notetaking solutions.
This is where Abridge comes in; it has developed a HIPAA-compliant AI platform that transcribes, structures, summarises, and provides insights from audio or transcripts of medical conversations. It claims to work across all specialities and offers patient-friendly summaries in easy-to-understand language.
Abridge aims to alleviate pressure on the healthcare system from the patient's perspective, a unique approach among healthtech startups. While companies like Inovia, DOUB, and DeepScribe focus on assisting physicians with medical transcription AI, they lack Abridge's patient-centric features, which are valuable in enhancing the patient experience. Its unique focus, then, is part of the reason why it enjoyed two impressive rounds of funding in 2025 alone.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 6 rounds)
May 2025
$300m
SERIES E
Feb 2025
$250m
SERIES D
Total funding: $757.5m
Company benefits
- Unlimited PTO, plus 12 national holidays
- 16 weeks paid parental leave, for all employees
- Flexible working hours — we care more about what you accomplish than what specific hours you’re working
- Remote work environment
- Equity for all new employees
- Generous equipment budget for your home office setup ($1600)
- Opportunity to work and grow with talented individuals, and have ownership and impact at a high growth startup
Company HQ
SoMa, San Francisco, CA
Leadership
Shivdev Rao
(CEO)Alongside their work as a practising Cardiologist at the University of Pittsburgh Medical Center, they also served as Physician Advisor in Residence and ultimately EVP at UPMC Enterprises; co-founded DocDok; and worked as a co-architect on a healthcare project at the Kauffman Foundation.
Sandeep Konam
(CTO)Completed an MS in Robotics at Carnegie Mellon, then served as Senior Product Manager at UPMC Enterprises. Has delivered guest lectures at Carnegie Mellon, Stanford, and the University of Pittsburgh.
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