Member of Technical Staff, Generative AI
AI in this role
About Abridge
Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.
Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.
We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.
The Role
We are looking for GenAI Software Engineers of all levels who are passionate about making a positive impact. 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.
What You’ll Do
Design and build agentic 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 agentic 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.
What You’ll Bring
3+ years of experience building production-grade systems, with 1–2+ years focused on LLM-powered or agentic 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.
Must be willing to work from our SF or NYC office at least 3x per week.
This position requires a commitment to a hybrid work model, with the expectation of coming into the office a minimum of (3) three times per week. Relocation assistance is available for candidates willing to move to San Francisco.
Why Work at Abridge?
At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month.
Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business.
Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients.
We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life.
If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you.
How we take care of Abridgers:
Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees
Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families.
Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA.
Paid Parental Leave: Generous paid parental leave for all full-time employees.
Family Forming Benefits: Resources and financial support to help you build your family.
401(k) Matching: Contribution matching to help invest in your future.
Personal Device Allowance: Tax free funds for personal device usage.
Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.
Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more.
Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals.
Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment.
Compensation and Equity: Competitive compensation and equity grants for full time employees.
... and much more!
Equal Opportunity Employer
Abridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability.
We're committed to providing reasonable accommodations throughout the interview process. Once you submit your application, we'll follow up with details on how to request an accommodation for interviewing, completing any assessments, or otherwise participating in the selection process.
Staying safe - Protect yourself from recruitment fraud
We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions.
How we rate this
Member of Technical Staff, Generative AI at Abridge rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you decide that one model's output is better than another's for a given task?
- What are the limits of LangChain that you've run into, and how did you work around them?
- What's a project where you used LlamaIndex hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: AI Agents, AI Evaluation, LangChain, and LlamaIndex. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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