Machine Learning Engineer
Build and deploy production ML models and intelligent services (including LLMs and RAG) that automate healthcare workflows at Plenful. Requires 5+ years ML/software engineering experience, strong Python skills, and familiarity with modern MLOps and cloud infrastructure.
About the job
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.)
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
Skills
Python, PyTorch, TensorFlow, scikit-learn, SQL, Docker, Kubernetes, AWS, MLflow, LangChain, Pinecone, LLMs, RAG, MLOps
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