Jobs in the United States

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Added 7 days ago
FIB-TEM Automation Engineer
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Strong AI and data science skills applied to FIB-TEM workflows (e.g.
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JR108247 Staff Thin Film Process Development Engineer
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WI
(USA) Principal, Software Engineer
Walmart Inc.
United States
- $110,000 to $220,000 USD / year
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WI
Distinguished, Software Engineer (Architect) | Omni Services | Sunnyvale
Walmart Inc.
United States
- $169,000 to $338,000 USD / year
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General Motors LLC
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For completed degrees, graduation must have occurred within the past 12 months
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Remote, United States
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Co-op: Manufacturing Engineer (Spring 2027)
Volvo Group
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Analyze manufacturing data to improve process capability, quality, and performance.
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Quality Engineer, AI & Test Automation
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AI Engineer (AI Accelerator Program)
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San Francisco, CA
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Amazon.com Services LLC
Austin, TX · Seattle, WA
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Added 7 days ago
Technical Program Manager - Frontier AI Data Labeling
Hippocratic AI
Menlo Park, CA
- Engineer data quality at scale. Design QC systems using gold sets, calibration sessions, spot checks, and inter-rater agreement tracking. Catch rater drift and annotation bottlenecks…
Added 7 days ago
Software Dev Engineer I - Security Platform
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Irvine, CA · San Francisco, CA
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Software Engineering Lead
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At EarthOptics we are combining cutting-edge genomic analysis, data science, microbiology, and remote sensing to revolutionize agriculture. Our mission is to be the leading soil information…
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AI Engineer
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- $190,000 to $215,000 USD / year
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Added 7 days ago
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Controls Engineer
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- $95,000 to $130,000 USD / year
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Added 7 days ago
Software Development Engineer, Network Monitoring and Remediation (NMR)
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Added 7 days ago
Forward Deployed Engineer (FDE), AWS Forward Deployed Engineering, US Government
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Added 7 days ago
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Senior Engineer – Spacecraft System Test Engineer (AI&T) - $15K Sign On Bonus
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Boulder, CO
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Hippocratic AI

Technical Program Manager - Frontier AI Data Labeling

Menlo Park, CA, United States

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Job details

Posting language: English
Posted: Nov 4, 2026

Original job description

ABOUT THE ROLE

Frontier models are only as good as the data they learn from. At Hippocratic AI, that data decides whether an AI agent safely guides a patient through a medication change at 2 a.m.

Our data powers Polaris, a constellation of models with a 700B-parameter core and specialized safety supervisors. In our evaluations, Polaris reaches 99.95% accuracy on drug-safety benchmarks versus 87.9% for the closest voice-deployable frontier model. The labeling, evals, and rater calibration behind it are how we earn and defend numbers like that.

We’re hiring a Technical Program Manager to run the human-in-the-loop data labeling engine behind our frontier healthcare AI. That spans audio and conversation annotation, clinician-labeled training data, preference and RLHF data, and the evaluations that decide what ships. You’ll work daily with ML researchers, applied scientists, and licensed clinicians. The data you produce feeds directly into our model training and evaluation loops.

WHAT YOU’LL DO

Run the data pipelines behind our models. Own end-to-end delivery of labeling and annotation programs, including audio and voice-conversation labeling, supervised labeling, and preference and RLHF data. You’ll manage scoping, task design, execution, QA, and delivery against live model-development cycles.
Turn evals into products. Work with ML and Applied Science to turn evaluation and training-signal needs into concrete labeling tasks, rubrics, and success criteria. Your evals become the benchmarks that gate releases.
Build and scale an expert rater workforce. Source, screen, and onboard clinicians and specialized raters through marketplaces, vendors, and in-house teams. This includes designing qualification tasks, setting rates, and scaling pools up and down as programs change.
Write the guidelines that shape model behavior. Author and iterate labeling guidelines and annotation rubrics, especially for subjective, high-stakes clinical judgment.
Engineer data quality at scale. Design QC systems using gold sets, calibration sessions, spot checks, and inter-rater agreement tracking. Catch rater drift and annotation bottlenecks before they reach training.
Drive velocity and accountability. Track throughput, cost, and quality. Manage vendor relationships (SOWs, performance reviews), choose labeling and QA tooling, and keep Engineering, Clinical, and Infrastructure aligned on dependencies and timelines.

WHAT SUCCESS LOOKS LIKE

By Day 90: You own a live data labeling or eval program through a major delivery milestone. You’ve cleared a critical bottleneck in the pipeline and shipped one measurable quality or throughput improvement.
By 12 months: You run multiple concurrent labeling and eval programs. Your calibrated rater bench is trusted by ML teams, and you can trace model improvements back to the data you delivered.

WHAT YOU BRING

2+ years in technical program management or data operations in an AI/ML or data-intensive environment (3+ years considered for exceptional candidates)
1+ years hands-on with data labeling, annotation, or model evaluation, including writing guidelines and managing quality at scale
Experience sourcing and managing contractor or expert annotator workforces through marketplaces (e.g., Mercor, Upwork, Toloka) or labeling vendors
Working knowledge of how annotation quality affects model training, including preference data, pairwise comparisons, and rater calibration (RLHF)
A strong grasp of quality metrics (IAA, precision/recall on gold sets, throughput vs. accuracy tradeoffs)
A track record of owning critical data or eval projects under time pressure, working with ML engineers and applied scientists, and driving results without direct authority
Comfort with ambiguity in a fast-moving, healthcare-adjacent AI environment

NICE TO HAVE

Experience running or supporting RLHF or other training-data pipelines at an AI lab or applied ML team
Clinical, healthcare, or other regulated, high-stakes data experience
Familiarity with Label Studio, Scale AI, Surge AI, or SageMaker Ground Truth
Experience qualifying specialized raters such as clinicians or bilingual SMEs
Exposure to reward hacking and specification gaming, and how annotation design can mitigate them

WHY JOIN HIPPOCRATIC AI

Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM - a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.

Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.

Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others.

Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies - ensuring our platform is powerful, trusted, and truly transformative.

EQUAL OPPORTUNITY

Hippocratic AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, genetic information, military or veteran status, or any other characteristic protected by applicable law. We are committed to building a team that reflects the patients we serve. We actively encourage applications from candidates of all backgrounds. If you require accommodations during the hiring process, please contact people@hippocraticai.com.

Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com http://hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.

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Jobs in the United States