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About This Role
Full Stack Engineer SFO, CA- Hybrid (Tuesday to Thursday in office) 6+ months with high possibility of extension
Job Description: Key Responsibilities:
- System Architecture & Database Design: Architect scalable, low-latency backend systems capable of handling massive datasets and live risk-data updates. Design highly optimized database schemas (SQL/NoSQL) and data orchestration pipelines tailored for FRM automation.
- API & Service Orchestration: Develop and manage RESTful APIs, GraphQL, and high-performance real-time communication layers (WebSockets/gRPC-web). Implement microservices architecture and Model Context Protocols (MCPs) to securely connect AI agents with internal enterprise data.
- Advanced AI & LLM Integration: Design, integrate, and deploy production-ready LLM systems using providers like OpenAI and Anthropic. Build sophisticated backend workflows using AI agent frameworks (Lang Graph, CrewAI), function calling, and complex tool-use patterns to automate business logic.
- Full-Stack Execution & Frontend Delivery: Translate high-fidelity Figma designs or rapid prototypes into pixel-perfect, scalable, and secure React layouts. Manage complex asynchronous workflows using Redux + Sagas or Zustand, while implementing TanStack Query for modern server-state synchronization.
- Performance & AI Observability: Build resilient AI systems by implementing AI Observability (e.g., LangSmith, Opik), hallucination detection, and retry logic. Optimize end-to-end performance to ensure frequent real-time updates do not trigger unnecessary UI re-renders, maintaining a fluid user experience even under high data loads.
- AI-Driven Development: Lead by example in using AI-augmented coding workflows, leveraging tools like Cursor, Codex, and GitHub Copilot to accelerate cross-stack feature delivery, refactor legacy logic, and eliminate boilerplate.
The Mission The Financial Risk Management (FRM) team ensures the integrity of our platform through real-time monitoring, complex data visualization, and AI-driven automation. We are looking for a Senior Full-Stack Engineer (Backend-Focused) to architect the core data systems and service layers that power our platform, while seamlessly tying them into high-velocity user interfaces. You will build highly scalable infrastructure, orchestrate complex APIs, integrate GenAI capabilities into our backend workflows, and deliver polished frontend experiences.
The Role: Architecting Intelligent, End-to-End Infrastructure You will own the system architecture for our FRM compliance and automation tools. This means moving fluidly across the entire stack-designing databases, building robust microservices, integrating Model Context Protocols (MCPs) for AI agents, and hardening interactive dashboards to handle live risk-data updates with ultra-low latency. You are a high-velocity engineer who embraces the future of software development, treating data, APIs, and interfaces as a single cohesive product.
Required Qualifications
- 7+ Years of Engineering Experience: A deep background in system design, database architecture, and scaling enterprise-grade backend services, with a recent pivot toward full-stack delivery and AI-augmented workflows.
- Core Stack Mastery: Deep proficiency in Python (Flask/FastAPI) and Node.js to develop performant service layers, combined with a strong command of React and TypeScript to build production-grade interfaces.
- Real-Time & Protocol Expertise: Expert-level knowledge of RESTful APIs, service orchestration, WebSockets, and gRPC-web to ensure data streams perfectly sync between backend nodes and client apps.
- State & Data Lifecycle Strategy: Expert implementation of high-throughput databases, caching layers (e.g., Redis), and modern state management strategies (TanStack Query, Zustand, or Redux) to handle heavy data mutations seamlessly.
- AI Tooling & LLM Orchestration: Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, Smolagents, DSPy) as well as advanced prompt engineering. Mastery of the Cursor code editor (indexing, composer, and chat features) to maintain elite velocity.
Nice to Have
- Uber Ecosystem: Prior experience at Uber or deep familiarity with our internal developer ecosystem, internal data pipelines, and platform services.
- Workflow Domain: Background in building systems for anomaly detection dashboards, compliance tools, or complex workflow-driven automation platforms (e.g., Celery, Cadence, Temporal).
- Rapid Prototyping Fluency: Familiarity with design-to-code or AI prototyping sandboxes (like Lovable or v0.dev) to quickly map out internal tools before building them into permanent architecture.
- Product Mindset: Proven focus on delivering measurable user value, using stakeholder feedback for iterative improvement, and treating technical components as a product to ensure usability, quality, and adoption.
The Tech Stack
- Backend Core: Python (FastAPI, Flask), Node.js, Microservices, MCP (Model Context Protocol).
- Frontend Core: React, TypeScript, Fusion.js (Uber’s Framework).
- AI & LLM Tools: Cursor, GitHub Copilot, LangGraph, CrewAI, LangSmith/Opik, Lovable, v0.dev.
- State & Data: Redux + Sagas, TanStack Query, Zustand, Context API, WebSockets, gRPC-web, GraphQL, Redis, SQL/NoSQL.
- Design & UI: BaseUI design system, Styletron (CSS-in-JS), Figma, Tailwind CSS.
Salary Context
This $176K-$187K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At RLink Solutions LLC, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $176K to $187K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
RLink Solutions LLC AI Hiring
RLink Solutions LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span San Francisco, CA, US, US. Compensation range: $145K - $187K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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