DeepScribe - AI Engineer

DeepScribe — AI Engineer Type: Full-time | Remote (US) — Bay Area residents encouraged to work from SF office | San Francisco, CA (preferred) Compensation: $150K – $250K + competitive equity Hiring count: 2 Visa sponsorship: No — TN available, no H-1B Reports to: Misha Bosin, Engineering Manager About DeepScribe DeepScribe is building AI agents to automate and transform clinical workflows — beyond note-taking, it positions itself as the "operating system for healthcare," embedding AI into the workflows of overburdened clinicians across clinical trial matching, billing, and ambient documentation. Powered by the largest clinical dataset in healthcare, its AI scribe ambiently captures patient visits and writes complete, billable documentation directly inside the clinician's EHR. The company is Series B with an oncology focus, and reports its platform reaching roughly 40% of U.S. patients. Founded: 2017 | Team size: 60 | Total funding: $61M (raised over $60M from top-tier investors including Index Ventures, plus angels Alexandr Wang (fmr CEO of Scale AI) and Dylan Field (CEO of Figma))Industry: Healthcare, AIWebsite: deepscribe.aiOffice: San Francisco, California, United States Trusted by major healthcare organizations including The US Oncology Network (the nation's largest oncology network) and Ochsner Health (the largest healthcare system on the Gulf Coast). Why Candidates Should Join End-to-end ownership: Own new AI workflows from prototype to production — LLM-powered apps, ambient copilots, billing automation, clinical trial matching. Real scale, real users: Improve an LLM-powered documentation platform used daily by thousands of clinicians. Strong backing: $61M raised, Index Ventures, and angels Alexandr Wang and Dylan Field behind the company. Genuine impact: Reduce clinician burnout and improve patient care, with adoption at the largest oncology network in the country. Intake Call Summary Series B startup founded in 2017, oncology focus, ~40% of U.S. patients on the platform. Values speed in development and impact; central mission is reducing healthcare burnout and improving outcomes. Hiring AI Engineers focused on NLP and audio processing; core product blends text and speech technologies. Role is full-stack — deploy solutions quickly and work across the entire stack. Targeting mid to senior candidates with flexibility for exceptional candidates (see Scoring Notes for the governing experience band). Must-have skills: Python, TypeScript, and a strong understanding of ML/AI technologies. Nice-to-haves: speech recognition, EHR systems, HIPAA-compliant data handling. Salary $150K–$250K depending on location and level; remote within the U.S., HQ in San Francisco. Interview process: EM screen, technical coding challenge, AI challenge, cultural fit; flexible scheduling with potential for combined interviews. Ideal profile: self-starters comfortable in fast-paced environments who deploy quickly; open to diverse backgrounds, valuing problem-solving and adaptability. Pain points: previously faced speech-to-text challenges (now resolved); high priority to fill the role promptly. The Role An AI Engineer to build and ship LLM-powered healthcare applications with ownership over new AI workflows from prototype to production. A deeply technical role blending ML, product thinking, and engineering — building and owning production services that rely on LLMs, speech models, and medical reasoning. What You'll Be Doing Develop and own end-to-end AI applications such as clinical trial matching, ambient copilots, and billing automation. Improve the LLM-powered documentation platform used daily by thousands of clinicians. Build and optimize inference pipelines and real-time speech recognition systems. Collaborate closely with PMs, designers, clinical experts, and GTM teams to iterate rapidly and launch high-impact features. Tech stack: Python, TypeScript, LLM tooling (LangGraph, Mastra, Agents SDK), LLM Evals + Applied GenAI Qualifications Seniority 2–6 years of experience as a software engineer, with work on AI and LLM-focused products [Required — but treated as a red flag for scoring; see Scoring Notes] Work Experience Must have work building complex AI applications end-to-end (LLM inference work, Agent products, etc.) [Must have] Experience in a fast-moving startup or as a founder [Strongly preferred] Education STEM undergraduate degree [Required] Hard Skills Able to work across the stack (frontend, backend, infra) to problem solve and ship features [Required] Applied-AI literacy — can reason about evals, statistics, and the non-determinism of LLM systems [Required] Familiarity with SOC-2, HIPAA, or sensitive data pipelines [Strongly preferred] Experience with EHR integrations (FHIR, HL7) or healthcare-specific ontologies [Strongly preferred] Soft Skills Obsessed with speed, ownership, and getting real user feedback [Strongly preferred] Traits to Avoid Lack of progression in their role after 2–3 years. (Red flag — triggers as