Conversational AI Engineer Jobs for OPT Students
Conversational AI Engineer roles are among the most OPT-friendly positions in tech right now, with strong demand from companies already experienced in sponsoring F-1 students. Most roles require a background in NLP, machine learning, or computer science, fields where STEM OPT's 24-month extension applies directly.
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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.
Known - 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 a Conversational AI Engineer
Target companies with active LCA filings
Companies that have filed Labor Condition Applications for similar AI or ML roles are already familiar with work authorization. Look for employers with a history of H-1B sponsorship in NLP or machine learning, as they're more likely to support OPT and future transitions.
Confirm your degree qualifies for STEM OPT
Conversational AI roles typically fall under computer science, electrical engineering, or data science CIP codes. Verify your degree program is on the STEM OPT designated list before applying, so you can truthfully state 24 months of remaining work authorization.
Lead with your NLP and LLM project work
Employers hiring for this role care about hands-on experience with language models, dialogue systems, or speech interfaces. Listing specific tools and architectures you've worked with, such as transformer-based models or intent classification pipelines, signals readiness without requiring extensive work history.
Prioritize startups building AI products
Early-stage companies developing voice assistants, chatbots, or customer-facing AI often move faster on hiring and have fewer HR layers. These employers frequently sponsor OPT students because specialized conversational AI talent is genuinely difficult to source elsewhere in the market.
Time your applications around OPT start dates
Most employers expect you to start within four to six weeks of an offer. Apply when your OPT EAD is approved or within 90 days of your program end date, so you can give a concrete and near-term start date without creating uncertainty for hiring managers.
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Get Access To All JobsFrequently Asked Questions
Do Conversational AI Engineer roles qualify for the STEM OPT extension?
Yes, in most cases. Conversational AI Engineering sits squarely within computer science, electrical engineering, or information technology, all of which appear on the STEM OPT designated degree list. Your degree program must carry an eligible CIP code, and the job must be directly related to that field. If your role involves NLP, dialogue systems, or machine learning, the connection is straightforward to document for your DSO.
How do I find Conversational AI Engineer jobs that sponsor OPT students?
Migrate Mate filters job listings specifically for roles open to F-1 OPT candidates, so you're not manually screening hundreds of postings. Search for Conversational AI Engineer or related NLP and ML titles on Migrate Mate to see employers actively hiring students on work authorization. This saves significant time compared to applying broadly and discovering sponsorship limitations late in the process.
What technical skills do employers look for in OPT candidates applying for this role?
Employers consistently prioritize experience with large language models, intent recognition, dialogue management frameworks, and speech-to-text pipelines. Familiarity with tools like Rasa, Dialogflow, or custom transformer fine-tuning stands out. OPT candidates who can demonstrate end-to-end project work, even from academic or internship settings, are competitive against candidates with longer work histories.
Will a company's H-1B sponsorship history tell me anything about OPT sponsorship?
Yes, it's a reliable signal. A company that has sponsored H-1B petitions for software engineers or AI researchers has already built the internal HR and legal infrastructure for work authorization. They understand the process, have immigration counsel, and are less likely to withdraw an offer due to OPT-related uncertainty. Filtering for these employers is one of the most efficient ways to protect your job search timeline.
Can I work as a Conversational AI Engineer at a company that doesn't plan to sponsor an H-1B later?
You can start on OPT without any H-1B commitment from an employer. However, if you want to stay in the U.S. long-term, you'll need a path to a different status before your OPT and any STEM extension expires. It's worth having a direct conversation about sponsorship intent before accepting an offer, rather than assuming it will be resolved later when your timeline is shorter.
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