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About Plenful Plenful is on a mission to transform healthcare operations from the inside out. Fresh off our $50M Series B and backed by Notable Capital, Bessemer Venture Partners, TQ Ventures, Susa/Kivu Ventures, and other leading investors, we’re building the category-defining AI workflow automation platform that healthcare teams rely on to operate smarter, faster, and more efficiently. Our technology empowers healthcare operators across hospital and health systems, pharmacies and payors to eliminate manual work, reduce administrative burden, and improve compliance, all while unlocking critical revenue to fund programs for their in-need patient populations. Built by healthcare operators for healthcare operators, Plenful is driven by a deep understanding of the challenges facing today’s care teams. We’re passionate about equipping healthcare workers with world-class tools that deliver real, measurable impact, and we’re proud to serve 90+ leading health systems across the country. If you’re excited to help shape the future of healthcare, we’d love to meet you. Apply now to join our growing team. About The Role We're looking for a Machine Learning Engineer to design, build, and deploy production-grade ML systems that power the next generation of Plenful's AI platform. You'll own the end-to-end lifecycle — from experimentation to production deployment to ongoing model performance. You'll partner closely with software engineers, product managers, and data teams to build models and intelligent services that automate healthcare workflows, improve operational efficiency, and create great user experiences. This is an engineering-focused role, and your work will directly impact customers. You'll thrive here if you enjoy solving hard problems with practical engineering solutions, take ownership from idea through production, and balance experimentation with delivering reliable software. We're a fast-moving startup where priorities evolve quickly — you should be energized by that, not worn down by it. What You'll Do Design, build, and deploy machine learning models into production Develop scalable ML pipelines for training, evaluation, monitoring, and inference Build intelligent services using modern NLP, LLM, classification, recommendation, and prediction techniques where appropriate Collaborate with Product and Engineering to translate customer problems into ML solutions Improve model performance through experimentation, feature engineering, and evaluation Work with structured and unstructured datasets to develop production-ready features Implement monitoring, observability, and retraining strategies to maintain model quality Optimize model latency, scalability, and infrastructure costs Contribute to architecture discussions and engineering best practices Stay current with advancements in machine learning and AI, and bring practical innovations into our platform You May Be a Fit If You have 5+ years of professional software engineering or machine learning engineering experience You have a Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field (or equivalent practical experience) You have strong programming experience in Python You've built and deployed machine learning models into production environments You have a solid understanding of supervised and unsupervised learning techniques You're familiar with modern ML infrastructure — classical MLOps (MLflow, Weights & Biases, Airflow) and LLMOps (LangFuse/LangSmith for tracing, Ragas/Braintrust for evaluation, vLLM/BentoML for serving, and a vector database such as Pinecone, Weaviate, or Qdrant for RAG pipelines) You've built data pipelines using SQL and distributed data processing tools You're familiar with cloud platforms such as AWS, GCP, or Azure You've deployed containerized applications using Docker and Kubernetes You have a strong grasp of software engineering fundamentals — testing, version control, and CI/CD You communicate well and collaborate easily across technical and non-technical teams Bonus Points If You Have worked with Large Language Models (LLMs), retrieval-augmented generation (RAG), embeddings, or agentic AI systems Have fine-tuned foundation models or worked with prompt engineering techniques Are familiar with ML infrastructure tools such as MLflow, Weights & Biases, Airflow, Kubeflow, or SageMaker Have experience with vector databases and semantic search technologies Have healthcare, pharmacy, or health tech experience Have worked in a startup or other fast-paced environment Technologies you'll likely work with: Python, PyTorch, TensorFlow, Scikit-learn, SQL, PostgreSQL, Docker, Kubernetes, AWS, GitHub Actions, REST APIs, vector databases, and LLM APIs (OpenAI, Anthropic, etc.) Why You'll Love Working Here 🚀 Mission-Driven, World-Class Team — Join an exceptional group of professionals aligned around a meaningful mission and committed to making an impact 📈 Opportunities for Growth — Strengthen your expertise through collaboration with experienced, high-performing leaders across the organization 🏢 Flexible Hybrid Work Environment — We're remote-first, with meaningful office presence in San Francisco and New York. R&D roles follow a hybrid model, with two days per week in our San Francisco office Benefits & Perks 🏥 Healthcare Coverage — Full medical, dental, and vision insurance for you and participation for your family 💰 401(k) with Company Match — Plenful matches 50% of your first 3% contributed 📊 Equity — Every full-time employee shares in our success 🌴 Unlimited PTO — Take the time you need, when you need it 🍽️ Daily Lunch Stipend — $100/week to cover your midday meals 💪 Wellness Stipend — $100/month to support your health and well-being 🚇 Commuter Benefits — $100/month for SF and NYC-based employees 👶 Parental Leave — Paid leave to support growing families
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.