Medeloop
At Medeloop, we’re building an AI-powered platform that streamlines and accelerates the most critical parts of scientific and clinical work through agentic workflows. Our modular tools—Grants Genius, Studies Genius, and Analytics Genius—help researchers secure funding, manage studies, and generate reproducible, privacy-preserving insights without writing a single line of code.
From grant writing to study execution to real-time analysis, Medeloop replaces manual effort with intelligent automation—helping research teams move faster, stay compliant, and unlock better outcomes. Originating from Stanford and backed by leading VCs, we’re partnering with top NIH-funded institutions to transform how science gets done.
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The Role
We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI — and a true passion for experimentation and creation — to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop’s technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem.
What You'll Own
- Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A).
- Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows).
- Develop rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production.
- Drive LLM and ML model development — train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges.
- Shape Medeloop’s agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership.
- Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI.
What We're Looking For
- 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains.
- Experience working on analytic engines (or advanced analytics platforms) — designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale.
- Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms.
- Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix).
- Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors.
- A builder/experimenter mindset — you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems.
- Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats.
Bonus Points
- Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility).
- Multi-cloud experience (AWS, Azure, GCP)
Why Medeloop
- Ownership from day one: small team, high-trust, no layers between your work and its impact
- Technically ambitious: you'll build AI-powered workflows, not just support them
- Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
- Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build
Ready to apply?
Apply to Medeloop
Senior Software Development Engineer in Test - SF
We're looking for a Senior SDET who thinks deeply about quality in systems that are inherently non-deterministic. Agentic AI doesn't fail the same way traditional software does — and testing it requires a new toolkit: eval frameworks, prompt regression, tool-call reliability, adversarial scenarios, and more.
You'll own the entire quality infrastructure across our product portfolio — from test data and CI pipelines to the standards and culture of how we ship. You'll work directly with product, devops, and AI engineering, with no layers between your decisions and their impact.
What You'll Own
- Test infrastructure, test data, test processes across the entire product portfolio while working with Devops and Infrastructure engineers
- Test Framework - build and enhance automated testing frameworks and tools that facilitate automated testing across different layers of application.
- The reliability bar for all Web applications, Mobile applications and AI agent outputs — from hallucination detection to latency regressions and tool-call correctness
- Test infrastructure, test data, and test processes across the entire product portfolio, alongside DevOps and Infrastructure engineers
- Automated testing frameworks that span all layers of the application — unit, integration, contract, and end-to-end
- Evaluation frameworks designed for LLM-based systems: non-deterministic output scoring, prompt regression, and adversarial test suites
- HIPAA-aware test data management — de-identification pipelines, synthetic data generation, and audit trail validation
- Integration of automated tests into CI/CD pipelines for continuous delivery confidence
- Build stability monitoring and release gate enforcement before any deployment
- Documentation of test plans, test results, and evaluation standards to support knowledge sharing
- The "safety net" for product quality — you define what done looks like
What We're Looking For
- 8+ years of hands-on SDET experience, with recent work building or testing agentic AI systems (single- or multi-agent) in production
- Experience in healthcare or life sciences — you understand what's at stake when a system fails in this domain
- A true tester's mindset: you seek out edge cases, adversarial inputs, and failure modes others overlook
- Proficiency across the full test pyramid — unit, integration, system, performance, and exploratory — plus familiarity with LLM-specific evaluation approaches
- Strong debugging skills across multi-tier web and mobile architectures; comfortable jumping into production incidents
- Proficiency with testing frameworks such as Jest, React Testing Library, Supertest, and pytest.
- Hands-on experience with testing tools like Cypress, Playwright, Supertest, and pytest (including requests or Selenium-based testing)
- Experience testing RESTful APIs using tools like Postman or Supertest
- Solid command of JavaScript and Python
Bonus Points
- Multi-cloud experience (AWS, Azure, GCP)
- Experience with red-teaming or adversarial testing of AI systems
- Native mobile testing experience (iOS, Android)
- Prior work with 21 CFR Part 11, GxP, or similar regulated-software validation frameworks
Why Medeloop
- Ownership from day one: small team, high-trust, no layers between your work and its impact
- Technically ambitious: you'll build AI-powered workflows, not just support them
- Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build
Ready to apply?
Apply to Medeloop
We are seeking a Senior DevOps & Site Reliability Engineer to own the reliability, scalability, performance, and operational excellence of Medeloop’s platform. This role blends deep DevOps engineering—CI/CD pipelines, infrastructure as code, and cloud architecture—with SRE discipline: SLOs, incident management, capacity planning, observability and a relentless focus on system uptime. You will be the bridge between development and operations, ensuring our clinical research products are always available, performant, and secure for the healthcare organizations that depend on them.
What You'll Own
Cloud Infrastructure & Architecture
- Design, implement, and manage scalable, secure, and highly available cloud infrastructure on AWS - infrastructure as code (IaC) using AWS CDK, CloudFormation, or Terraform, ensuring all environments are version-controlled and reproducible.
- Architect multi-region and disaster recovery strategies that meet healthcare uptime requirements.
- Manage containerized workloads using Docker and Kubernetes, optimizing for cost, performance, and resilience.
Site Reliability Engineering
- Define, implement, and monitor Service Level Objectives (SLOs) and Service Level Indicators (SLIs) across all production services.
- Build and maintain observability stacks (DataDog, AWS CloudWatch, Sentry) covering metrics, logs, traces, and alerting.
- Lead incident response: triage, mitigate, and drive blameless post-incident reviews with actionable follow-ups.
- Conduct capacity planning and performance engineering to ensure the platform scales ahead of demand.
- Champion error budgets and use them to balance feature velocity with system stability.
- Identify, assess, and mitigate operational risks by collaborating with engineering and product teams to evaluate impact and likelihood before they become incidents.
- Participate in and help structure an on-call rotation, ensuring clear escalation paths and fair distribution of after-hours coverage.
CI/CD & Automation
- Build self-service tooling and runbooks that reduce toil and empower development teams to ship independently.
- Design and maintain CI/CD pipelines (GitHub Actions) that enable fast, safe, and repeatable deployments.
- Automate security scanning (SAST, DAST) within pipelines and collaborate with engineering to remediate findings.
- Implement progressive delivery strategies such as canary deployments, blue-green releases, and feature flags.
- Proficiency in scripting languages (Python, Bash) for automation, troubleshooting, and building reliability tooling.
- Track and drive down operational toil, targeting less than 50% of team time spent on repetitive manual work.
- Evaluate and manage change risk for production deployments, maintaining change review processes that balance speed with stability.
Security & Compliance
- Ensure infrastructure meets healthcare compliance standards (HIPAA, SOC 2) through policy-as-code, encryption, and access controls.
- Manage networking security (VPCs, subnets, security groups, WAFs) and identity/authentication systems (AWS Cognito, Auth0, OAuth2, SSO).
- Conduct regular security reviews, vulnerability assessments, and patching across the infrastructure estate.
Collaboration & Culture
- Partner closely with product and engineering teams to embed reliability thinking into the software development lifecycle.
- Develop and maintain comprehensive documentation for infrastructure, runbooks, and operational playbooks.
- Mentor junior engineers on DevOps and SRE best practices, fostering a culture of ownership and continuous improvement.
- Stay current with advancements in cloud technologies, DevOps tooling, and SRE methodologies.
- Own and evolve internal developer platform tooling — including deployment workflows (GitOps/Flux), bug tracking integrations, and developer self-service portals.
What We're Looking For
- Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
- 7+ years of combined experience in DevOps and/or Site Reliability Engineering roles, with at least 2 years in a senior capacity.
- Deep proficiency with AWS services
- Deep experience with observability and monitoring platforms such as DataDog, AWS CloudWatch, and Sentry.
- Strong experience building and maintaining CI/CD pipelines with GitHub Actions or equivalent tools.
- Expertise in infrastructure as code using AWS CDK, CloudFormation, or Terraform.
- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
- Proven track record of defining and operating against SLOs/SLIs and managing incident response processes.
- Solid understanding of networking (VPCs, subnets, load balancing, DNS), security, and compliance best practices.
- Experience with authentication and authorization systems including AWS Cognito, Auth0, OAuth2, and SSO.
- Proactive, self-directed mindset with a bias toward action and taking initiative.
- Excellent problem-solving skills and the ability to work independently as well as collaboratively across teams.
- Strong communication skills—able to explain complex infrastructure decisions clearly to technical and non-technical stakeholders.
- Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats.
Bonus Points
- Multi-cloud experience (AWS, Azure, GCP)
- Familiarity with healthcare data standards, compliance, and protocols such as HIPAA, HL7 FHIR, OMOP, and i2b2.
- Experience with chaos engineering practices and tools (e.g., AWS Fault Injection Simulator, Gremlin).
- Prior experience in a healthcare or life sciences company operating under strict regulatory requirements.
- Contributions to open-source infrastructure or SRE tooling.
- Relevant certifications such as AWS Solutions Architect, Certified Kubernetes Administrator (CKA), or Google SRE certification.
Why Medeloop
- Ownership from day one: small team, high-trust, no layers between your work and its impact
- Technically ambitious: you'll build AI-powered workflows, not just support them
- Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
- Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build
Ready to apply?
Apply to Medeloop