Senior Backend Engineer - AI/ML

About Onfire Health

Onfire Health is an early-stage startup committed to making functional medicine and longevity care accessible and affordable for all. Engineering is central to our offering, as we are building a platform that incorporates multiple revenue channels and agentic payments management.

Our core platform is a payments and group-buying membership which allows members to fund their out-of-pocket health expenses with zero-interest using their savings plan. It leverages group-negotiated, discounted services using pre-planned savings instead of relying on expensive consumer debt.

What You'll Do

• Design and build scalable backend services and APIs that power AI/ML features serving millions of requests per day
• Architect and optimize machine learning pipelines for training, evaluation, and real-time inference
• Build and maintain robust data infrastructure for large-scale model training and experimentation
• Implement vector databases, embedding systems, and semantic search capabilities
• Work with LLMs and implement RAG (Retrieval-Augmented Generation) architectures
• Optimize system performance, reduce latency, and improve model serving efficiency
• Collaborate with ML researchers to productionize cutting-edge models and algorithms
• Mentor junior engineers and contribute to technical strategy and architecture decisions
• Participate in on-call rotation and ensure system reliability and uptime

Required Qualifications

• 8+ years of backend engineering experience with production systems at scale
• Expert-level Python proficiency with deep understanding of async/await, concurrency, and performance optimization
• Strong experience building and deploying ML/AI systems in production environments
• Hands-on experience with modern ML frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers
• Solid understanding of ML fundamentals: model training, evaluation, hyperparameter tuning, and deployment
• Experience with LLMs, prompt engineering, fine-tuning, and inference optimization
• Proficiency with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes)
• Strong grasp of distributed systems, microservices architecture, and API design
• Experience with SQL and NoSQL databases (PostgreSQL, Redis, MongoDB, etc.)
• Track record of shipping high-quality code quickly in fast-paced environments

Tech Stack

Nice to Have

• Experience with healthcare systems (HIPAA compliance, HL7/FHIR standards, EHR integrations)
• Experience with payment systems and financial infrastructure (PCI-DSS compliance, Stripe, payment gateways)
• Experience with model optimization techniques (quantization, distillation, pruning)
• Knowledge of MLOps best practices and tools (MLflow, Weights & Biases, Kubeflow)
• Experience with real-time streaming and event-driven architectures
• Contributions to open-source ML/AI projects
• Experience with GPU optimization and distributed training
• Background in NLP, computer vision, or recommendation systems
• Understanding of model interpretability and responsible AI practices
• Experience scaling systems from 0 to millions of users

Compensation

As an early-stage startup, we offer a competitive and performance-driven total compensation package that includes a significant equity component. The specific salary and stock option grant will be highly competitive and commensurate with your experience, skill level, and the impact you bring to the role.