Machine Learning Engineer

Tala Health; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Tala Health in San Francisco, CA is hiring a Machine Learning Engineer to turn clinical data into world‑class AI that clinicians can trust from day one. You’ll fine‑tune foundation models and ship models that support the full patient journey, balancing innovation with privacy and safety requirements.

You'll use Python, PyTorch/TensorFlow, and HuggingFace, building scalable training pipelines and contributing to model‑serving integrations (RAG, LoRA, quantization) for low‑latency inference in

Qualifications

• 4+ years of ML engineering or research with deep learning.
• Proficiency in Python, PyTorch or TensorFlow.
• Experience with distributed training and model evaluation.
• Familiarity with healthcare data standards (HIPAA) is a plus.
• M.S. or Ph.D. in CS/EE/Stats or related field preferred.

Responsibilities

• Fine-tune and retrain large language and multimodal models on de-identified clinical data sets.
• Build scalable training pipelines (data prep, augmentation, distributed jobs).
• Design automated evaluation frameworks to track accuracy, bias, and safety metrics.
• Run experiments and hyperparameter sweeps; document insights.
• Collaborate with Data, Product and Clinical teams to iterate rapidly.
• Contribute to model-serving integrations (RAG, LoRA, quantization) for low-latency inference.

Skills

Python

PyTorch

TensorFlow

HuggingFace

Distributed training

Model fine-tuning

Research experience

GPU/TPU orchestration

Education

MS/PhD in CS/EE/Stats or related

Tools

DDP

DeepSpeed

RAG

LoRA

Quantization

HuggingFace

Job description

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.