Machine Learning Engineer

Tala Health was built to transform a healthcare system that remains slow, expensive and inefficient. Today’s patients often face a fragmented system that requires juggling doctor visits, lab work, referrals and long wait times just to reach a diagnosis. With Tala Health, patients will receive a new kind of care experience that brings AI agents and clinicians together from the start to deliver accurate, personalized care faster. We are building AI agents to support the full arc of the patient journey. The Opportunity: Machine Learning Engineer Help us turn domain‑specific data into world‑class clinical AI. You’ll fine‑tune foundation models, craft rigorous evaluation suites and ship models that clinicians can trust on day one, balancing innovation with the safety, privacy and bias‑constraints that healthcare demands. What You'll Do Fine‑tune and retrain large language and multimodal models on de‑identified clinical data sets. Build scalable training pipelines (data prep, augmentation, distributed jobs) using PyTorch or TensorFlow. Design automated evaluation frameworks to track accuracy, bias, hallucination and safety metrics. Run systematic experiments and hyper‑parameter sweeps; document and share insights. Partner with Data, Product and Clinical teams to gather feedback and iterate rapidly. Contribute to model‑serving integrations (RAG, LoRA, quantization) for low‑latency inference in prod. What You Bring 4+years of ML engineering or research, including hands‑on deep‑learning model training. Expertise with Python, PyTorch/TensorFlow, HuggingFace, and distributed training (DDP, DeepSpeed). Familiarity with GPU/TPU orchestration, performance tuning and cost optimization. Bonus: exposure to healthcare data standards (FHIR, HL7) and privacy frameworks (HIPAA). M.S. or Ph.D. in CS, EE, Statistics or related field—or equivalent applied experience. #J-18808-Ljbffr Tala Health