Build a pipeline for parsing clinical documents and validating executable modules.
Design and refine a target syntax for representing guideline logic.
Run experiments across open and closed models, fine-tuning approaches, and constrained decoding.
They build a compiler that turns clinical guidelines into structured, executable decision logic for healthcare. The team is research-focused with production engineering stakes, working closely with clinicians and technical leadership.
Own the build out of new agents, skills, and platform capability for teams across TLDR.
Build and deploy agents end to end, from design through implementation, evals, and rollout to internal users.
Partner with stakeholders across sales, editorial, and people ops to find where an LLM belongs in their process.
TLDR runs the largest network of tech newsletters in the world, with over 8 million subscribers covering startups, software engineering, AI, and more. Our 31-person full-time team is bootstrapped, profitable, and on track for $35M in revenue this year, with a culture of owning functions rather than slices.
Build the component layer around our layout synthesis engine, including API contracts, services, and evaluation gates.
Turn model retraining into a one-command job with built-in benchmarks and readable results for the whole team.
Own latency and cost budgets for learned capabilities and partner with infrastructure engineers on MLOps and deployment.
Higharc is a VC-backed startup that is changing how new homes are designed and built using spatial AI and generative floor plan technology. The company is fully remote, has raised over $175M, and values flexibility, collaboration, and asynchronous deep work.
Design and build AI agents and automation to solve real problems across engineering, product, and delivery.
Partner with stakeholders to identify high-leverage opportunities and deliver end-to-end solutions.
Stay current with LLM and agentic frameworks to drive innovation in healthcare technology.
HealtheDGE provides AI-powered operational infrastructure for health insurance companies, helping them modernize operations. The company is experiencing strong market momentum and invests in its people, offering a collaborative culture focused on innovation.
Build and ship AI methods for federal research data with LLMs, NLP, RAG, knowledge graphs.
Benchmark AI against baselines and human reviewers to prove gains in accuracy, reliability, efficiency, cost.
Set standards for trustworthy AI and mentor data scientists through code review and guidance.
ARI builds AI methods for federal research-funding and administrative data, with clients including the NIH. It is a small multidisciplinary team of scientists, analysts, economists, and data scientists experienced with NIH data.
Design, build, and deploy LLM-powered product features, including lab summaries and conversational agents.
Build backend services integrating LLMs and ML models, primarily using Python with exposure to Elixir.
Implement evaluation, monitoring, and CI/CD workflows for AI features, ensuring reliability and clinical relevance.
Fullscript is a health technology platform that helps practitioners deliver better care through clinical insights, lab interpretations, and patient analytics. With over 125,000 practitioners and 10 million patients, the company emphasizes a people-first culture, teamwork, and continuous learning in a remote-first environment.
Owns the architectural direction for Code Metal's AI platform and the team building it.
Builds production-grade inference serving, model gateways, agent harnesses, and context-engineering services.
Instruments the stack end to end and drives experimentation and observability for reliable AI systems.
Code Metal provides automated software engineering using AI and formal verification to produce provably correct code. Founded in 2023 and backed by Accel, Salesforce Ventures, and others, it serves defense and enterprise customers from offices in Boston and San Francisco.
Own the design and delivery of data engineering and clinical NLP pipelines that transform structured and unstructured EMR data into standardized clinical data.
Develop hybrid approaches using rules, dictionaries, deep learning, and LLMs for clinical entity extraction, normalization, and context interpretation.
Partner with clinicians to define schemas, validate OMOP CDM mappings, and ensure production reliability for transplant-focused data.
CareDx is a precision medicine diagnostics company advancing care in transplant, specialty oncology, and cell therapy through molecular diagnostics, digital health solutions, and AI-powered analytics. The company fosters a collaborative culture where every employee contributes to innovations that transform patient care.
Build AI features inside real clinical workflows, like checking notes against payer and compliance requirements before finalization.
Design evals to prove those features are right and keep them honest as models change.
Prototype AI capabilities, including voice agents, and take the best from prototype to production.
Ease builds the AI-native operating system for behavioral health, replacing disconnected tools with one platform for CRM, EHR, and revenue cycle management. Backed by a $41M Series A, this rapidly growing NYC team has no product managers—engineers talk to customers and ship in days.
Develop and improve AI methods, algorithms, and systems across the lifecycle of reliable AI agents.
Create new approaches for simulating, evaluating, and optimizing agent behavior in real-world settings.
Turn research ideas into working systems, prototypes, and production-facing capabilities.
The company builds infrastructure that helps enterprises make AI agents more reliable in production. It is a technical and research-focused team working on AI methods and practical systems.
Develop AI-based biomarkers using multimodal data including whole-slide images, clinical variables, and molecular data to predict patient outcomes and treatment benefit.
Contribute to self-supervised foundation models and downstream ML models such as multiple-instance learning, survival analysis, segmentation, and classification.
Improve model robustness and interpretability across scanners, institutions, and patient populations to build clinician trust.
Artera is an artificial intelligence company dedicated to transforming cancer care by developing foundation models that analyze clinical and pathology data to guide therapy selection. They value diversity and collaboration, offering a supportive remote culture with unlimited PTO and equity as core compensation.
Design and implement RAG pipelines and tool-calling architectures for conversational AI agents in healthcare.
Build production Python code using LangChain and modern LLM frameworks to solve novel healthcare AI problems.
Own end-to-end deployments, monitoring, and customer partnerships in production healthcare environments.
Hippocratic AI is building the first healthcare-only, safety-focused large language model to transform patient outcomes. The company is backed by leading healthcare and AI investors, with a team of physicians, hospital leaders, and AI pioneers from top institutions.
Curate high-quality code examples and datasets for LLM training and evaluation.
Develop and assess AI-generated software across multiple programming languages and the full SDLC.
Collaborate with research teams to design verification mechanisms and improve coding benchmarks.
This partner company specializes in evaluating large language models and improving AI systems through rigorous engineering benchmarks. It offers a remote, collaborative culture where engineers and researchers advance AI evaluation workflows together.
Work directly with leading AI labs and enterprises to define research goals and technical requirements.
Build data intelligence systems and implement ML pipelines for data curation, model training, and evaluation.
Develop LLM applications, including multi-agent systems, RAG workflows, and evaluation harnesses.
Our client is a venture-backed AI company building intelligent systems by combining human expertise with machine learning. With over $40 million in funding and a global expert network, they provide critical infrastructure for AI development.