About this role
Monaco is building an AI-native revenue platform that replaces the fragmented GTM stack (CRM, sequencing, call recording, enrichment, pipeline management) with one unified system. We’re consolidating 6–10 disconnected tools into a single, purpose-built platform and redefining what’s possible when all the data lives under one roof in the age of AI - this is a category-defining shift, not an incremental improvement.
We launched publicly in Feb 2026 and are 50 people and growing. We have strong early product-market fit, creating millions in ARR in only a few months post-launch. Opportunities exist in both scaling core systems and workflows, and building new cutting edge features from 0 to 1.
We’ve raised $85M through our Series B from legendary investors including Founders Fund, Benchmark, and Human Capital. Our founders are industry veterans who previously led companies like Brex, Apollo, and Clari.
Come join us if you want to be part of a high autonomy, high pace team reinventing one of the biggest categories in enterprise software.
We're looking for an AI Engineer to help turn modern foundation models into real, reliable product features. This role is about applied AI - prompts, orchestration, retrieval, and product integration - not training models from scratch.
You'll work closely with product and design to build AI capabilities that users actually trust and use every day.
What you'll do:
- Build LLM-powered product features using prompt engineering, structured outputs, and tools.
- Build and iterate on RAG systems (chunking, embeddings, retrieval, prompt composition).
- Orchestrate multi-step AI workflows: agents, tools, memory, retries, fallbacks.
- Evaluate quality, latency, and cost - and continuously improve reliability.
What we're looking for:
- 3+ years building AI-driven or ML-adjacent product features.
- Strong hands-on experience with LLMs (OpenAI, Anthropic, or open-source).
- Solid Python skills and experience integrating AI into backend systems.
- Product-minded: you care about UX, speed, and real-world constraints.
Nice to have
- Experience building agentic systems with tools and memory.
- Familiar with vector databases and embedding models.
- Some exposure to fine-tuning or adapters.
Location:
San Francisco. We're an in-person team - 5 days in the office. At this stage, proximity genuinely accelerates product quality and team cohesion.
We launched publicly in Feb 2026 and are 50 people and growing. We have strong early product-market fit, creating millions in ARR in only a few months post-launch. Opportunities exist in both scaling core systems and workflows, and building new cutting edge features from 0 to 1.
We’ve raised $85M through our Series B from legendary investors including Founders Fund, Benchmark, and Human Capital. Our founders are industry veterans who previously led companies like Brex, Apollo, and Clari.
Come join us if you want to be part of a high autonomy, high pace team reinventing one of the biggest categories in enterprise software.
We're looking for an AI Engineer to help turn modern foundation models into real, reliable product features. This role is about applied AI - prompts, orchestration, retrieval, and product integration - not training models from scratch.
You'll work closely with product and design to build AI capabilities that users actually trust and use every day.
What you'll do:
- Build LLM-powered product features using prompt engineering, structured outputs, and tools.
- Build and iterate on RAG systems (chunking, embeddings, retrieval, prompt composition).
- Orchestrate multi-step AI workflows: agents, tools, memory, retries, fallbacks.
- Evaluate quality, latency, and cost - and continuously improve reliability.
What we're looking for:
- 3+ years building AI-driven or ML-adjacent product features.
- Strong hands-on experience with LLMs (OpenAI, Anthropic, or open-source).
- Solid Python skills and experience integrating AI into backend systems.
- Product-minded: you care about UX, speed, and real-world constraints.
Nice to have
- Experience building agentic systems with tools and memory.
- Familiar with vector databases and embedding models.
- Some exposure to fine-tuning or adapters.
Location:
San Francisco. We're an in-person team - 5 days in the office. At this stage, proximity genuinely accelerates product quality and team cohesion.
Tech stack
LLMPython
About Monaco
Monaco is hiring for the ai engineer role. NewJob aggregates active openings directly from Monaco's applicant tracking system, so this listing is current.
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