About the Position
We are seeking an experienced Lead Machine Learning Engineer to own the strategy and execution of our ML systems. The ideal candidate will bring deep expertise in natural language processing (NLP) and a proven track record of building production-ready ML systems in the healthcare sector. You will set the technical direction for LLM inference and BERT-based models that power our platform, driving expansion into new medical domains. Experience with transformer architectures and large language models is required.
This is a senior technical leadership role. You will be the primary ML decision-maker, mentoring a growing team while staying hands-on with model development and system architecture.
Our Stack:
Azure, Python, PyTorch/TensorFlow, PostgreSQL
Key Responsibilities
Technical Leadership & Strategy
- Define and execute the ML/NLP strategy for expanding into new medical domains (pathology, cardiology, oncology) beyond our current radiology focus
- Make critical architecture and build-vs-buy decisions for LLM inference, model serving, and NLP pipelines
- Evaluate and integrate emerging LLM technologies and open-source models, balancing innovation with production readiness
- Serve as the primary technical advisor on all ML-related roadmap, investment, and hiring decisions
Hands-On Engineering
- Design, develop, and deploy production NLP models to extract clinical insights from unstructured medical text
- Architect and optimize LLM inference pipelines for low-latency, high-reliability performance at scale
- Fine-tune and optimize transformer-based models (BERT, GPT, T5, or similar) for healthcare-specific NLP tasks, including entity extraction, classification, and recommendation parsing
- Build and maintain scalable ML pipelines for model training, evaluation, and deployment
- Implement model monitoring, versioning, and continuous improvement processes
Team & Cross-Functional Collaboration
- Mentor and grow a team of ML engineers and data scientists, establishing engineering standards and best practices
- Collaborate with product, engineering, and clinical teams to translate clinical requirements into ML solutions
- Work with customer-facing teams to ensure reliable model performance in production environments
- Communicate ML progress, risks, and impact to both technical and non-technical stakeholders
- Develop automated testing suites for ML systems and lead code review practices for the ML team
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related field (or equivalent experience)
- 5+ years of experience developing and deploying NLP/ML models in production settings, with at least 2 years in a technical leadership or lead role
- Deep hands-on experience with transformer architectures (BERT, GPT, T5, or similar) and large language model inference optimization
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or Hugging Face Transformers)
- Strong understanding of model evaluation metrics, A/B testing, and ML best practices
- Proven ability to lead technical teams, conduct architecture reviews, and drive engineering quality
- Experience designing and operating MLOps infrastructure: CI/CD for models, experiment tracking, model versioning, and production monitoring
- Excellent communication skills and ability to collaborate effectively across engineering, clinical, and executive teams
- Ability to thrive in a fast-paced startup environment and adapt to changing priorities
Nice to Have
- Experience with LLM serving frameworks (vLLM, TensorRT-LLM, Triton Inference Server)
- Familiarity with medical NLP benchmarks and datasets (MIMIC, i2b2, n2c2)
- Experience building retrieval-augmented generation (RAG) systems for domain-specific knowledge bases
- Experience working with healthcare data and understanding of HL7 v2, FHIR, or other healthcare data exchange standards
- Background in radiology informatics or clinical decision support systems
How to Apply
Please submit your resume and a cover letter outlining your qualifications and interest in the position to careers@inflohealth.com. Include any relevant GitHub repositories or portfolio links demonstrating your work.
Inflo Health is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
