AI in Medical & Healthcare — Remote / Online / Part-time / Full-time Careers Playbook

Levi Cheptora

Wed, 29 Oct 2025

Hello — this guide is for practitioners, clinicians, data scientists, ML engineers, product managers and policy people who want remote / online / part-time / full-time careers using AI in medicine and healthcare. Read this as a hands-on playbook: what roles are actually hiring, what to learn and certify, how to build a portfolio that gets interviews, where to apply (100+ live links), and which myths to stop believing.

I’ve included practical guidance for technical and non-technical backgrounds (clinicians, public-health professionals, data scientists, product and regulatory hires). Scroll, pick a lane, and use the job tracker idea at the end.

1 — Quick orientation: what remote AI-in-health roles actually exist

Common remote/online roles (full-time, part-time, contract / consultancy)

• Applied ML / Clinical ML Engineer (imaging, EHR, time-series)

• Data Scientist (predictive models, cohort discovery, outcome modelling)

• MLOps / ML Platform Engineer (reproducible pipelines, model deployment)

• Clinical AI Researcher / Health Data Scientist (research & model validation)

• Product Manager — AI Health Products (regulatory-aware product owners)

• AI Clinical Validator / Clinical Scientist (retrospective validation, study design)

• Data Engineer (ETL, FHIR/DICOM pipelines, data governance)

• Health Informatics Engineer (HL7/FHIR integration, interoperability)

• Model Risk & AI Governance Specialist (bias audits, model cards, explainability)

• Regulatory & Quality Specialist (AI SaMD compliance, clinical evidence strategies)

• Clinical Data Annotator / Labeler (part-time, remote)

• Technical Writer / Documentation Engineer (model cards, clinical protocols)

• Research Engineer / Intern (Kaggle/PhysioNet projects → portfolio)

Companies ranging from enterprises (cloud vendors, pharma) to AI startups hire remote for many of these roles — from large teams running platform work (NVIDIA, Google Cloud) to clinical AI startups and consultancies. NVIDIA+1

2 — Career paths & degrees that move the needle (practical map)

Technical paths

• Entry → Mid → Senior: Data Analyst → Data Scientist → Applied ML Engineer → Lead ML Engineer / ML Platform / Head of Clinical AI

• Common degree background: CS / Data Science / Statistics / Biomedical Engineering; clinical backgrounds (MD, RN, PharmD) + a strong data portfolio are highly prized for clinical translation roles.

• Useful advanced degrees: MSc Data Science, MSc Bioinformatics, MPH with quantitative focus, MSc Clinical Informatics, PhD (method/research roles).

Non-technical/clinical paths

• Clinical scientist / validator / regulatory: MPH/MPA or MSc Health Informatics + clinical credentials (or clinical experience) — excellent for bridging clinicians and engineers.

• Product & policy: MBA or MPP/MPA + domain AI knowledge helps in product strategy and commercialization.

Practical credential checklist (hireable / widely recognized)

• Cloud & ML Certifications: Google Cloud Professional Data Engineer / Vertex AI skills, AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer.

• Model & Framework: TensorFlow Developer Certificate, PyTorch courses (DeepLearning.AI).

• MLOps & Platform: Kubeflow, MLflow, Kubernetes certifications (CKA) are big pluses.

• Healthcare-specific: HL7 / FHIR fundamentals, DHIS2 (for global health data pipelines), Good Clinical Practice (GCP) / CITI Program for work with clinical data.

• Ethics & governance: Certifications or short courses in AI ethics, algorithmic fairness, GDPR/HIPAA awareness.

Which to pick first? For immediate remote hiring: pick one cloud cert + one practical ML for medicine specialization (see resources) and build 2 reproducible notebooks on public clinical datasets. Coursera+1

3 — Free & paid learning (role-targeted)

High-value free / low-cost resources

• AI for Medicine Specialization (deeplearning.ai / Coursera) — applied imaging, prognosis and NLP for healthcare. Coursera+1

• PhysioNet / MIMIC (public clinical datasets) — practice EHR/time-series modelling after completing credentialing. PhysioNet

• Kaggle — competitions and public notebooks for reproducible portfolios.

• Google Cloud, AWS, Microsoft Learn — free hands-on labs and product docs (Vertex AI, SageMaker, Azure ML).

• Papers With Code & arXiv — keep current with healthcare AI research and reproducible code.

Paid (worth it for speed & hiring)

• Coursera Specializations (verified certificates) — AI for Medicine, ML engineering specializations.

• Google Cloud / AWS / Azure paid cert exams — visible signal on LinkedIn and résumés.

• NVIDIA Deep Learning Institute — applied GPU & medical imaging modules.

• University short exec programs (Stanford, HBS Online, MIT xPro) — useful for senior career pivots.

Practical micro-learning: run one small end-to-end project (data ingestion → model → validation → model card → deployment demo) and publish as a 3-slide summary + reproducible notebook.

4 — Datasets & environments to practice on (portfolio-grade)

• MIMIC (PhysioNet) — ICU EHR data (requires credentialing). PhysioNet

• Kaggle healthcare datasets — chest X-ray sets, ECG datasets, COVID datasets.

• NIH & TCIA — imaging collections (CT/MRI) for imaging work.

• Open Payments / CMS / public claims data — useful for market-level research.

• Synthetic health datasets — for privacy-preserving prototyping.

Always document data provenance, consent/IRB status, and privacy safeguards in your portfolio.

5 — Portfolio hacks & strategies that win remote AI-in-health interviews

Make your portfolio auditable, reproducible and clinical-aware.

1. One-page project case studies (x3) — problem → dataset → model & evaluation → clinical/operational impact → limitations & next steps. Include code link (GitHub) + a short video walkthrough (3–4 minutes).

2. Reproducible notebooks — Binder/Colab links that run end-to-end (data ingest → feature engineering → model training → evaluation figures).

3. Model cards & datasheets — short standard documents explaining model purpose, intended use, performance by subgroup, known failure modes, and governance controls (helps interviews).

4. Regulatory/validation artifacts — a mock clinical validation plan (pre-specifying metrics, sample size, endpoints) and a redacted retrospective validation report. That shows you know clinical evidence requirements.

5. Deployment demo — a tiny web UI or notebook that shows inference on a sample (hosted on Hugging Face Spaces, Streamlit, or a simple Heroku/Vercel demo).

6. Bias & fairness audit — short section showing you checked for distributional shifts and subgroup performance; list mitigations.

7. Explainability artifacts — SHAP/Integrated Gradients visualizations with clinical interpretation notes.

8. Security & privacy notes — brief mention of de-identification, encryption, access controls, and HIPAA/GDPR considerations.

Presentation: put everything behind a single, short public portfolio URL (GitHub + one-page README) and feature that link on LinkedIn / CV.

6 — Debunking common myths (evidence & what actually works)

Myth — “AI in health is only for PhDs.”
Reality — many product, MLOps, data-engineering and clinical validation roles hire MSc/MPH/MD + demonstrable project work. Practical, reproducible projects trump degree alone for many remote roles. (See how corporate AI teams hire across levels at Google & NVIDIA). Google Health+1

Myth — “You must have access to private hospital EHRs.”
Reality — public datasets (MIMIC, TCIA) + synthetic datasets + well-constructed transfer learning pipelines can build a credible portfolio for hiring. Credentialing & IRB needed for private EHR work, but many early hires are evaluated on public, reproducible work. PhysioNet

Myth — “AI will replace clinicians.”
Reality — the bulk of roles are collaborative: clinical AI augments workflows — product, validation and governance roles require human clinical oversight. Hiring demand exists for clinicians who can translate clinical needs to technical teams. Evidence: startups and enterprise teams routinely hire clinical specialists and clinical validators (see PathAI, Viz.ai, Tempus jobs). PathAI+2viz.ai+2

7 — Practical job-search & application hacks (remote-first)

• Search with filters: use job portals’ filters for “remote”, “hybrid”, “flexible”, and “contract / consultant”.

• Two-pronged apply: (1) apply through the company careers page, (2) message a hiring manager or a team member on LinkedIn with a one-line value pitch + direct portfolio link.

• One-pager for hiring managers: 1-page “why me” for the role — 3 bullets of impact, 1 figure, and links to code + demo.

• Recruiter outreach template (2 lines): “Hi [Name] — I’m a data scientist/MD who built an X-ray triage model (AUROC 0.92) and a deployable Streamlit demo. I applied for [Role] — can I share the 1-page validation summary?”

• Open source signal: contribute to a medical AI repo, or publish a small utilities library (data loaders, DICOM helpers) — open source contributions are high-visibility proof of skill.

8 — 100+ categorized hiring sites, companies, learning & marketplaces (live links)

Below are 100+ live links organized so you can scan and apply fast. Bookmark and build a job tracker (Platform | Link | Role Type | Remote? | Date Applied | Contact).

Note: I pulled authoritative career pages, cloud & platform providers, AI-health startups, job boards, and training pages. For learning & dataset pages I cited central resources above. PhysioNet+3Google Health+3NVIDIA+3

A — General jobs & remote job platforms

1. LinkedIn Jobs — https://www.linkedin.com/jobs/

2. Indeed — https://www.indeed.com/

3. Glassdoor — https://www.glassdoor.com/Job/index.htm

4. Wellfound (AngelList) — https://wellfound.com/

5. ZipRecruiter — https://www.ziprecruiter.com/

6. Remote.co — https://remote.co/remote-jobs/

7. FlexJobs — https://www.flexjobs.com/

8. WeWorkRemotely — https://weworkremotely.com/

9. Remote OK — https://remoteok.com/

10. Built In (Tech jobs) — https://builtin.com/jobs

B — Tech & AI job boards (data & ML focused)

11. ai-jobs.net — https://ai-jobs.net/

12. Kaggle Jobs / Community — https://www.kaggle.com/

13. KDNuggets Jobs — https://www.kdnuggets.com/jobs/index.html

14. Papers With Code — https://paperswithcode.com/jobs

15. Stack Overflow Jobs (developer roles) — https://stackoverflow.com/jobs (useful for ML engineers)

16. Otta (startups) — https://otta.com/

17. TechCareers / Dice — https://www.dice.com/

C — Cloud & platform providers hiring in healthcare AI

18. Google / Google Health Careers — https://careers.google.com/ and https://health.google/our-mission. Google Careers+1

19. NVIDIA Careers & Healthcare hub — https://www.nvidia.com/en-us/about-nvidia/careers/ and https://www.nvidia.com/en-us/industries/healthcare-life-sciences/. nvidia.wd5.myworkdayjobs.com+1

20. Microsoft Careers / Health Futures — https://careers.microsoft.com/ and https://www.microsoft.com/en-us/research/lab/microsoft-health-futures/opportunities/. Microsoft Careers+1

21. Amazon / AWS Careers (Healthcare & ML roles) — https://www.amazon.jobs/ and https://aws.amazon.com/careers/. amazon.jobs+1

22. IBM / watsonx AI careers — https://www.ibm.com/careers/ (search watsonx / AI & healthcare roles). IBM

23. Google Cloud (Vertex AI) learning & jobs — https://cloud.google.com/vertex-ai

D — Leading AI-in-health startups & companies (careers)

24. Tempus — https://www.tempus.com/about-us/careers/ (clinical AI & GenAI roles). Tempus

25. PathAI — https://www.pathai.com/careers/ (pathology AI & clinical networks). PathAI

26. Viz.ai — https://www.viz.ai/careers/ (AI care coordination). viz.ai

27. Caption Health — https://www.captionhealth.com/careers (AI ultrasound guidance). LinkedIn

28. Recursion Pharmaceuticals (recursion.com/careers) — https://www.recursion.com/careers

29. Atomwise — https://www.atomwise.com/careers

30. Insilico Medicine — https://insilico.com/careers (drug discovery AI)

31. Owkin — https://owkin.com/careers (AI for clinical research)

32. Freenome — https://www.freenome.com/careers (early cancer detection AI)

33. Paige (digital pathology) — https://www.paige.ai/careers

34. Aidoc — https://www.aidoc.com/careers (radiology AI)

35. Qure.ai — https://qure.ai/careers (medical imaging AI)

36. Lunit — https://www.lunit.io/en/careers (medical image AI)

37. Zebra Medical Vision (now part of Nanox? check current employer page) — https://www.zebra-med.com/careers (verify live).

38. Bay Labs / Caption? (see Caption Health above)

39. Buoy Health — https://www.buoyhealth.com/careers

40. Ada Health — https://ada.com/careers

41. Babylon Health — https://www.babylonhealth.com/careers

42. Butterfly Network — https://www.butterflynetwork.com/careers (device + AI)

43. Paige.ai / Roche (check careers) — https://www.paige.ai/careers

44. Healx — https://healx.io/careers (AI drug repurposing)

45. BenevolentAI — https://benevolent.com/careers

Tip: many of these companies maintain remote or hybrid roles for ML engineers, product and clinical validation specialists — check their careers pages and set job alerts.

E — Pharma & medtech employers (AI teams & roles)

46. Roche — https://www.roche.com/careers.htm

47. Novartis — https://www.novartis.com/careers

48. Johnson & Johnson — https://www.careers.jnj.com/

49. Pfizer — https://www.pfizer.com/careers

50. GE Healthcare — https://www.gehealthcare.com/company/careers

51. Siemens Healthineers — https://www.siemens-healthineers.com/en-us/careers

F — Research & academic institutions (AI + medicine teams)

52. MIT CSAIL / Jameel Clinic — https://jameelclinic.mit.edu/ (watch careers pages)

53. Stanford AI in Medicine groups (see Stanford careers / labs) — https://med.stanford.edu/

54. Johns Hopkins AI & Data Science (public health & machine learning) — https://publichealth.jhu.edu/ (careers & research)

55. IHME (Institute for Health Metrics & Evaluation) — https://www.healthdata.org/about/careers

G — Health systems & hospital AI teams (often remote/part-time data roles)

56. Mayo Clinic Center for AI — https://www.mayoclinic.org/about-mayo-clinic/careers

57. Mass General Brigham AI initiatives — https://www.massgeneralbrigham.org/careers

58. NHS Digital & NHS AI labs (UK roles) — https://www.england.nhs.uk/ai/ and https://www.jobs.nhs.uk/

59. Cleveland Clinic — https://jobs.clevelandclinic.org/

H — Clinical research / CROs & translational AI roles

60. IQVIA — https://www.iqvia.com/careers (real world evidence & AI analytic teams)

61. Parexel — https://www.parexel.com/careers (data & AI in trials)

62. ICON plc — https://careers.iconplc.com/ (clinical data science)

63. Syneos Health — https://careers.syneoshealth.com/ (commercial analytics)

I — Marketplaces & freelance platforms (contract & part-time AI work)

64. Upwork — https://www.upwork.com/ (data science, ML contracts)

65. Toptal — https://www.toptal.com/ (senior ML talent)

66. Catalant — https://gocatalant.com/ (consulting experts for health tech)

67. Kolabtree — https://www.kolabtree.com/ (scientific freelancers)

68. GLG (expert network) — https://glginsights.com/

J — Data & reproducibility resources

69. PhysioNet / MIMIC — https://physionet.org/content/mimiciii/ (EHR dataset). PhysioNet

70. NIH Data Commons — https://datacommons.nih.gov/

71. TCIA (The Cancer Imaging Archive) — https://www.cancerimagingarchive.net/

72. UK Biobank — https://www.ukbiobank.ac.uk/ (access governed)

73. Hugging Face (models & Spaces) — https://huggingface.co/

K — Conferences & career hubs (networking & hiring)

74. NeurIPS (health workshops & career booths) — https://nips.cc/

75. AMIA (American Medical Informatics Association) — https://www.amia.org/ (jobs & networking)

76. RSNA (radiology & AI jobs) — https://www.rsna.org/

77. HIMSS — https://www.himss.org/ (health IT careers & vendors)

78. AI Med — https://ai-med.io/ (AI in medicine conferences & jobs)

L — Training & certification pages

79. Coursera AI for Medicine (deeplearning.ai) — https://www.coursera.org/specializations/ai-for-medicine. Coursera

80. DeepLearning.AI — https://www.deeplearning.ai/courses/ai-for-medicine-specialization/. DeepLearning.ai

81. Google Cloud Certifications — https://cloud.google.com/certification

82. AWS Machine Learning Specialty — https://aws.amazon.com/certification/certified-machine-learning-specialty/

83. TensorFlow Developer Certificate — https://www.tensorflow.org/certificate

84. NVIDIA Deep Learning Institute — https://developer.nvidia.com/dli

M — Regulatory, governance & standards (jobs + guidance)

85. FDA (Digital Health Center of Excellence / careers) — https://www.fda.gov/ (search digital health center/evidence guidance & careers)

86. EMA (European Medicines Agency) — https://www.ema.europa.eu/en/about-us/jobs

87. HL7 / FHIR resources — https://www.hl7.org/fhir/ (standards & learning)

88. RAPS (Regulatory Affairs Professionals Society) — https://www.raps.org/

N — News, industry trackers & job feeds (stay current)

89. Labiotech (biotech + AI) — https://www.labiotech.eu/

90. Stat News — https://www.statnews.com/ (health tech hiring signals)

91. FierceBiotech / FierceHealthcare — https://www.fiercebiotech.com/

92. TechCrunch (health AI coverage) — https://techcrunch.com/tag/healthcare/

O — Recruiters & executive search (AI + life sciences)

93. Kincannon & Reed — https://www.k-r.com/

94. EPM Scientific — https://epmscientific.com/

95. Michael Page — https://www.michaelpage.com/ (tech & life sciences)

96. TalentRise / specialized AI recruiters — search on LinkedIn for “AI healthcare recruiter”

P — Useful tooling & platforms to list on your CV

97. GitHub — https://github.com/ (public projects)

98. Hugging Face — https://huggingface.co/ (model sharing & Spaces demo)

99. Streamlit — https://streamlit.io/ (interactive model demos)

100. Tableau Public — https://public.tableau.com/ (demo dashboards)

101. Docker Hub / Docker — https://hub.docker.com/ (containerized demos)

102. MLflow — https://mlflow.org/ (experiment tracking)

Q — Extra: fellowships, research & grants for AI in health

103. NIH grants & training — https://www.nih.gov/grants-funding

104. Wellcome Trust (AI + health grants) — https://wellcome.org/

105. Chan Zuckerberg Initiative (AI in science) — https://www.chanzuckerberg.com/

9 — How to prioritize your 30/60/90 learning & job plan (practical)

Days 1–30 — Focus

• Choose a role (Applied ML / MLOps / Clinical Validation / Product). Build one reproducible project (notebook + model card + demo).

• Update LinkedIn headline to include “AI for Healthcare” + portfolio URL. Apply to 10 roles (use company career pages and 3 job boards).

Days 31–60 — Credential & yield

• Complete one cloud/ML certification module (Google Cloud or AWS). Publish a short validation report (1–2 pages) for your project and share as feature on LinkedIn.

• Do one small paid freelance pilot (Upwork/Toptal or contract via company careers).

Days 61–90 — Scale outreach

• Apply to 30 roles, reach out to 3 hiring managers/week with a one-page case study. Attend 2 conferences or meetups (virtual) and post a 3-minute demo video from your portfolio.

10 — Interview prep cheats for AI in health (remote)

• STAR + numbers: prepare 3 stories (model development, deployment bug/fix, clinical validation) with clear metrics.

• Clinical translation story: explain how you would move a model from retrospective validation → prospective pilot → regulatory submission (high level but concrete).

• Model evaluation: be able to explain calibration, AUROC vs AP, subgroup performance, and how you’d monitor model drift in production.

• Explainability & safety: show one example of how you used SHAP/IG to debug a model or find a data leakage issue.

• Regulatory awareness: know what “SaMD” means and be ready to discuss evidence generation plans and post-market monitoring ideas.

11 — Rates & salary framing (global & cautious)

Salaries vary by company, region and seniority. Rather than fixed numbers, aim to price by impact & deliverables:

• Junior data scientist / ML engineer (remote contract): price per project or month (e.g., small teams often use $3k–8k/month as a guideline, vary widely).

• Mid-level applied ML engineer / data scientist: full-time salary depends on region; freelance daily rates for senior technical roles often range from hundreds to low-thousands USD per day.

• Consulting / clinical validation leads: package by milestone (protocol → retrospective analysis → prospective pilot) with fixed fees per milestone.

(When negotiating, ask about “job grade” or “salary band” and compare against cloud/tech benchmarks for your region.)

12 — Ethics, safety & red flags to watch for

• Red flag: companies asking you to train models on private healthcare datasets without clear data governance, IRB, or contracts.

• Red flag: promises of immediate regulatory approval without rigorous clinical evidence.

• Must-have: contractual clarity on IP, authorship, data handling, and liability. For clinicians working with startups, insist on clear indemnity & compliance clauses.

• Do: ask for the target use case, data provenance, intended deployment environment and monitoring plan before accepting clinical model work.

13 — Final myth-busting & encouragement

• Remote AI-in-health roles are real and flexible — from part-time labeling gigs to full-time ML platform engineers and remote clinical validation scientists. Large cloud vendors and focused startups both hire remote talent. NVIDIA+1

• Build one reproducible, clinically-aware project and show the clinical/operational impact — that beats a generic CV in interviews. Use MIMIC/Kaggle for practice and publish model cards. PhysioNet+1