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About This Role
About Curie
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Curie is a telehealth platform that combines clinical expertise with AI to deliver personalized, accessible care. Our platform guides patients through intelligent intake, matches them with the right treatments, and supports clinicians with AI\-powered tools — all built to be safe, compliant, and scalable.
Our team includes ex\-founders, clinicians, and engineers from leading institutions such as Stanford, Harvard, UCLA, Berkeley, and AWS. We're well\-funded, growing aggressively, and building core infrastructure that will power the future of how patients access care.
The Role
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We're looking for a Senior AI Engineer to design and build the AI systems at the center of Curie's clinical platform. You'll own the Python and Go service layer that powers our clinical AI processing — from multi\-step intake reasoning to retrieval\-augmented generation for treatment guidance. This is an opportunity to shape the AI architecture of a healthcare product from the ground up, working closely with founders, clinicians, and engineers across the stack.
What You'll Build
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### Agentic Clinical Workflows
- Design and implement multi\-step AI agent pipelines that process patient intake, synthesize medical history, and surface clinical recommendations.
- Build orchestration patterns for managed, observable AI/ML workflows on cloud infrastructure.
- Own session and memory management for long\-running clinical agents — ensuring continuity, safety, and auditability across patient interactions.
### Retrieval \& Medical Knowledge Systems
- Build and optimize RAG pipelines that ground clinical AI outputs in authoritative medical guidelines, drug references, and treatment protocols — including embedding models, vector stores, and reranking strategies.
- Improve retrieval accuracy, citation traceability, and relevance ranking to ensure AI\-surfaced information is trustworthy and explainable.
- Continuously evaluate and iterate on retrieval quality with structured benchmarks.
### Clinical Data Infrastructure
- Extend and maintain the Python service that communicates with other microservices, handling structured clinical data and LLM integrations.
- Build data pipelines for ingesting, normalizing, and reconciling health data from external partners and EHR integrations.
- Design systems that respect HIPAA requirements end\-to\-end — from data handling to model I/O to audit logging.
### Observability \& Safety
- Instrument AI workflows with tracing, logging, and evaluation hooks for compliance\-grade visibility into model behavior.
- Build validation layers and guardrails that ensure clinical outputs meet safety thresholds before reaching patients or providers.
- Monitor failure rates, latency, and model drift across production AI systems.
How We Work
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- Small, senior team — high ownership, low overhead. You'll ship real features, not decks.
- Move fast with good judgment — we value pragmatism over perfection and ship iteratively.
- AI coding tools encouraged. We use what works.
What You Bring
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- 7\+ years of software engineering experience, with meaningful time building production AI/ML systems.
- Strong Python expertise — you're comfortable with async services, and modern tooling (uv, Pyright, etc.).
- Familiarity with PyTorch, TensorFlow, or Hugging Face Transformers for custom model work.
- Hands\-on experience with LLMs in production: prompt engineering, structured output, evaluation, and iteration — across commercial APIs (OpenAI, Anthropic, Google) or open\-source models (LLaMA, Gemma, etc.).
- Experience building RAG pipelines, vector search, or retrieval systems for grounding LLM outputs — using tools like LangChain, LlamaIndex, or custom implementations.
- Familiarity with agentic AI patterns — multi\-step reasoning, tool use, and orchestration frameworks (LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent).
- Comfort working across service boundaries — you can navigate a Go backend, gRPC interfaces, and cloud infrastructure when needed.
- Strong intuition for system design that balances correctness, observability, and performance.
- Curiosity about healthcare and a desire to build AI that's safe, explainable, and clinically useful.
Bonus Points
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- Experience with cloud ML platforms: GCP/Vertex AI, AWS SageMaker, or Azure ML.
- Hands\-on with local/self\-hosted LLM inference: vLLM, Ollama, TGI, or GGUF\-based deployments.
- Fine\-tuning or distillation experience — LoRA, QLoRA, RLHF, DPO, or similar techniques.
- Familiarity with model evaluation frameworks (RAGAS, DeepEval, custom evals) and LLM observability tools (Langfuse, LangSmith, Arize, Weights \& Biases).
- Familiarity with healthcare data standards (FHIR, HL7\) or EHR integrations.
- Background in medical AI safety, bias detection, or clinical validation.
- Experience with PostgreSQL (including JSONB, pgvector), sqlc, or gRPC/Connect\-RPC.
- Startup experience — especially as a founder, founding engineer, or early employee.
- Published work or deep domain knowledge in healthcare AI or clinical NLP.
Why Curie
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- Shape the AI architecture of a healthcare product from day one — your decisions will directly impact patient care at scale.
- Work alongside a world\-class team of engineers, clinicians, and ex\-founders who've built and scaled products before.
- Competitive salary, significant equity, and benefits in a well\-funded company with aggressive growth targets.
- Build with modern infrastructure: Vertex AI, SageMaker, AI frameworks, Go \+ Python — no legacy baggage.
- Direct impact on making quality healthcare more accessible.
Compensation Range: $180K \- $275K
Salary Context
This $180K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CURIE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($227K) sits 36% above the category median. Disclosed range: $180K to $275K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CURIE AI Hiring
CURIE has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, IL, US. Compensation range: $225K - $275K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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