Senior Applied AI Engineer (End-to-End ML)
Location:
Palo Alto, CA (Hybrid )
Role Type:
Full-Time / Permanent
Summary
Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below.
Our client, a pioneering HealthTech AI firm in the Bay Area, is seeking a high-calibre
Applied AI Engineer
to bridge the gap between advanced Machine Learning and robust Software Engineering. This is an end-to-end ownership role: you will be responsible for designing the logic, building the architecture, and deploying the final services.
Core Responsibilities
Architect AI Workflows:
Design and implement sophisticated agentic workflows and automation sequences that power clinical decision-making.
System Design & Integration:
Build the backend infrastructure, scalable REST APIs, and data services required to support high-concurrency AI applications.
Rapid Deployment:
Maintain a high-velocity shipping cycle, moving from prototype to production-grade implementation in days.
Model Orchestration:
Select, fine-tune, and evaluate the performance of various LLMs (including OpenAI, Anthropic, and open-source models) for specific healthcare tasks.
Full-Stack ML:
Own the pipeline from data ingestion and time-series forecasting to real-time classification and model monitoring.
Technical Profile
Computer Science Mastery:
Expert knowledge of algorithms, data structures, and distributed systems.
Software-Heavy Background:
Professional-grade Python skills. You should be comfortable with software design patterns, testing, and CI/CD.
Machine Learning Fundamentals:
* Deep understanding of
Core ML topics : classification, regression, and clustering.
Specific experience in
Time Series Forecasting
and temporal data analysis.
Proficiency in
Generative AI : RAG architectures, prompt optimization, and agent frameworks.
Infrastructure:
Experience deploying services to cloud environments (GCP preferred) and a solid grasp of MLOps and pipeline automation.
Education:
BS in Computer Science or related field + 4 years of experience, or an MS + 2 years of experience.
Cultural Fit
Startup Agility:
You possess the "scrappiness" to solve problems with limited resources but the rigor to ensure those solutions are enterprise-grade.
The "Generalist" Mindset:
You enjoy working across the entire stack and are not afraid to dive into data engineering or infrastructure when needed.
Mission-Oriented:
You are motivated by the prospect of using AI to significantly improve patient outcomes and healthcare efficiency.
What’s Offered
Our client provides a highly competitive package, including a strong base salary, meaningful equity, and comprehensive premium healthcare benefits. xsgimln You will join a world-class team of engineers in a collaborative, hybrid environment.