Machine Learning Engineer

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 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

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.)

🚀 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