AI Engineer Jobs in USA with Visa Sponsorship
AI engineers who specialize in taking models from research prototypes to production-grade systems are in surging demand across US companies scaling their AI capabilities. Unlike research-focused roles, AI engineering emphasizes MLOps, model serving, and building reliable inference pipelines - skills that are scarce and highly valued by employers filing sponsorship petitions. If you can bridge the gap between a trained model and a product that serves millions of users, US companies will sponsor your visa to do exactly that. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsKnown - Conversational AI Engineer, System Prompt and Orchestration
- Location: San Francisco, CA (In-Person)
- Compensation Range: $170k-$220k Cash + Equity
Known is a matchmaker that talks to users and supports them like a friend. Our mission is to empower humanity by applying general intelligence to human connection.
Users join Known by telling us their life story. On average, our new users talk to our AI voice agent for 27 minutes, giving us a uniquely intimate multi-modal data set.
We are a team of engineers who’ve created some of the most widely used AI-driven consumer products including Uber Eats, Uber, Faire, and Afterpay.
We love to work hard, with a high degree of autonomy and ownership. We work together in Cow Hollow, San Francisco.
About the Role
We’re looking for founding Conversational AI Engineers to build the prompt systems powering our voice-led onboarding and user experiences.
This is a unique opportunity to work with a hyper-personalized data-set, combining voice transcripts, images, and structured user data to empower real-time, personalized AI voice-led conversations at scale. You’ll work directly with Chen Peng, former head of ML at Uber Eats and Faire.
What You’ll Do
- Prompt Orchestration & Context Optimization: Architecting the core system prompts and managing context windows to ensure highly responsive, contextually relevant, and logically sound AI reasoning without bloating token counts or causing latency spikes.
- EQ & Semantic Memory: Building prompt systems that allow Known to maintain a consistent, empathetic, and uniquely "Known" personality. You'll design mechanisms to seamlessly weave long-term user memories and preferences into real-time dialogue, while helping the user drive the conversation.
- Conversational Intelligence: Designing advanced prompt chains (and fallback logic) to gracefully handle conversational tangents, user interruptions, semantic end-of-turn conversation logic, and complex emotional states so Known feels empathetic and responsive.
- Agentic Workflow Design: Implementing and maintaining the prompt-driven logic for multi-agent frameworks, where your system instructions act as the routing engine between the user, external APIs, and our internal matchmaking engine.
- Evals for Conversational Quality: Developing custom evaluation frameworks to measure "conversational success." You'll go beyond basic fact-checking to rigorously assess conversational dynamism, warmth, engagement, and hallucination reduction.
Requirements
We’re looking for someone who can make automated systems feel undeniably natural:
- 2-3 Years in Conversational AI/NLP: Proven experience designing, testing, and deploying complex LLM applications and system prompts in high-traffic production environments.
- The Prompt Stack: Deep familiarity with state-of-the-art prompt engineering techniques (e.g., Few-Shot, Chain-of-Thought, ReAct).
- Agentic & RAG Architectures: Experience building the "brain" logic for LLMs using frameworks like LangGraph, LlamaIndex, or Haystack to manage complex, non-linear dialogue and dynamic knowledge retrieval.
- Production Hardened: You treat prompts as an engineering problem. You’ve optimized prompt systems for scale, API cost, and speed. You're comfortable with prompt version control, programmatic prompt optimization (e.g., DSPy), and building continuous integration pipelines for AI evals.
Our Investors
We’re backed by Eurie Kim and Kirsten Green at Forerunner Ventures (the investors behind Decagon, Faire, and Oura), NFX, and PearVC.
How to Get Visa Sponsorship as an AI Engineer
Position yourself at the intersection of AI and engineering
AI Engineer is a fast-growing title that bridges ML research and production software. Highlight experience deploying models, building RAG pipelines, or integrating LLMs into products - these applied skills are what companies are hiring for.
Target companies actively building AI engineering teams
Companies from startups to enterprises are creating new AI engineering roles and sponsoring H-1B petitions to fill them. Databricks, Notion, Figma, and similar companies have expanded AI headcount specifically because demand outpaces domestic supply.
Clarify your SOC code with your employer
AI Engineer roles can file under SOC 15-1252 (Software Developers) if engineering-focused or 15-2051 (Data Scientists) if research-heavy. The right choice depends on your day-to-day duties - work with your employer's immigration attorney to match accurately.
Use STEM OPT to build production AI experience
A STEM-eligible degree gives you up to 3 years of work authorization through OPT. Ship real AI features during that time - companies are far more likely to sponsor an engineer who already knows their codebase and deployment stack.
Differentiate yourself with full-stack AI skills
Many AI engineers can fine-tune models but struggle with production infrastructure. Expertise in model serving, API design, latency optimization, and monitoring makes you harder to replace and gives employers a stronger reason to sponsor.
Consider the O-1 path if you have notable AI work
Significant open-source contributions, widely used AI tools, or media coverage of your work can support an O-1 extraordinary ability petition. You don't need a PhD - demonstrated impact in applied AI can qualify.
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Get Access To All JobsFrequently Asked Questions
What is the difference between an AI engineer and a machine learning engineer for visa sponsorship purposes?
For immigration purposes, the distinction matters less than how the employer describes the role in the petition. AI engineer roles tend to emphasize building end-to-end AI-powered products, integrating LLMs, and designing retrieval-augmented generation systems. ML engineer roles focus more on model training infrastructure and serving pipelines. Both qualify as specialty occupations, and the key is specificity in the job description listing the technical skills required and the degree fields that prepare someone for the work.
How important is production experience versus research experience for AI engineer visa sponsorship?
Production experience is the differentiator for AI engineering roles. Employers sponsoring AI engineers are specifically looking for someone who can deploy and maintain AI systems in production, handling model versioning, building evaluation frameworks, and optimizing inference costs. While research experience shows technical depth, the visa petition for an AI engineer role will emphasize production engineering requirements. Highlight systems you have built and shipped, along with experience in tools like Kubernetes, Docker, and model serving frameworks.
Do I need a master's degree or PhD to get sponsored as an AI engineer?
A bachelor's degree in computer science or a related field is sufficient for most AI engineering positions. Unlike AI research roles where a PhD is often expected, AI engineering emphasizes practical systems-building skills over academic credentials. That said, a master's degree qualifies you for the additional 20,000 H-1B lottery slots reserved for advanced degree holders and signals deeper technical knowledge. Many successful AI engineer petitions are filed with bachelor's-level candidates who have strong production ML experience.
Which companies are most actively sponsoring AI engineers?
Companies scaling AI products are the most active sponsors. This includes large tech firms (Google, Meta, Amazon, Microsoft), AI-first startups (OpenAI, Anthropic, Cohere), and AI infrastructure companies (Databricks, Anyscale, Weights & Biases). Enterprise companies integrating AI into their products across healthcare, finance, and e-commerce are also growing sponsors. Look for employers whose job descriptions specifically mention LLM integration, RAG systems, or production AI deployment, as these roles are distinct enough to produce strong petitions.
What technical skills should I emphasize in an AI engineer visa petition?
Focus on skills that distinguish AI engineering from general software development. LLM integration, vector database management, prompt engineering pipelines, inference optimization, and retrieval-augmented generation architecture are the most relevant. Experience with evaluation frameworks for AI systems, cost optimization for GPU workloads, and monitoring for model quality in production also carries weight. The more specific the technical requirements in the petition, the stronger the specialty occupation argument.
What is the prevailing wage requirement for sponsored AI Engineer jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a ai engineer in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.
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