P
Senior AI Engineer
People In AI
1 hour ago
Full-time
On-site
San Francisco, California, United States
Location:
San Francisco, hybrid
Employment Type:
Full-time
A fast-scaling, well-funded B2B SaaS company building AI-native software for complex, high-value professional workflows.
This team is rethinking how highly skilled professionals interact with complex data, documentation, and knowledge-heavy processes. Following significant recent funding and continued commercial growth, AI has become a central part of the company’s product and engineering strategy.
Rather than adding lightweight AI features around an existing product, the engineering organization is investing heavily in the underlying platform required to make production AI reliable: evaluation, ingestion, observability, retrieval, orchestration, and the infrastructure supporting increasingly capable AI workflows.
What You’ll Do
Build and scale core AI platform capabilities across evals, ingestion, observability, retrieval, and AI/ML infrastructure
Own AI systems end-to-end, taking ideas from prototype through to reliable production deployments
Design and improve RAG pipelines, embedding workflows, prompt systems, retrieval strategies, and model interaction patterns
Develop infrastructure for evaluating, monitoring, versioning, and improving LLM-powered systems
Build tooling around AI workflow orchestration, quality control, experimentation, and production reliability
Work with complex, domain-specific datasets to improve retrieval and model performance
Partner closely with product, design, engineering, and subject-matter experts to turn sophisticated workflows into usable AI products
Help establish technical patterns and engineering standards for applied AI across the wider organization
What You’ll Bring
Strong software engineering experience across areas such as backend engineering, distributed systems, infrastructure, developer platforms, or ML systems
Experience shipping AI or ML systems into production rather than working exclusively on prototypes or research
Practical experience with one or more of LLMs, RAG, NLP, AI agents, model evaluation, observability, data ingestion, or AI/ML infrastructure
Strong understanding of software architecture, system design, reliability, and production engineering
Ability to operate comfortably across both AI-specific problems and traditional software engineering challenges
A high bar for engineering quality, including the ability to critically review AI-generated code rather than treating generated output as production-ready
A pragmatic builder mentality and an appetite for high ownership in a fast-moving technical environment
Python
LLMs / Generative AI
Retrieval-Augmented Generation (RAG)
Embeddings & semantic retrieval
LLM evaluation frameworks
AI observability & monitoring
Workflow orchestration
AI / ML infrastructure
Distributed backend systems
Why Join? This is an opportunity to join at an important inflection point: the company has strong funding, an established product and customer base, and executive-level commitment to making AI a fundamental part of the platform.
You’ll have significant ownership over the infrastructure that determines whether AI systems actually work in production, from how information enters the system and is retrieved, through to how outputs are evaluated, monitored, and continuously improved.
About People In AI People In AI is a specialist recruitment partner dedicated to connecting exceptional AI, Machine Learning, Data, and Software Engineering talent with some of the most ambitious technology companies in the market.
We work closely with technical founders and engineering leaders to identify opportunities where talented engineers can have genuine impact, combining deep technical understanding with a highly personalized recruitment experience.
#J-18808-Ljbffr
San Francisco, hybrid
Employment Type:
Full-time
A fast-scaling, well-funded B2B SaaS company building AI-native software for complex, high-value professional workflows.
This team is rethinking how highly skilled professionals interact with complex data, documentation, and knowledge-heavy processes. Following significant recent funding and continued commercial growth, AI has become a central part of the company’s product and engineering strategy.
Rather than adding lightweight AI features around an existing product, the engineering organization is investing heavily in the underlying platform required to make production AI reliable: evaluation, ingestion, observability, retrieval, orchestration, and the infrastructure supporting increasingly capable AI workflows.
What You’ll Do
Build and scale core AI platform capabilities across evals, ingestion, observability, retrieval, and AI/ML infrastructure
Own AI systems end-to-end, taking ideas from prototype through to reliable production deployments
Design and improve RAG pipelines, embedding workflows, prompt systems, retrieval strategies, and model interaction patterns
Develop infrastructure for evaluating, monitoring, versioning, and improving LLM-powered systems
Build tooling around AI workflow orchestration, quality control, experimentation, and production reliability
Work with complex, domain-specific datasets to improve retrieval and model performance
Partner closely with product, design, engineering, and subject-matter experts to turn sophisticated workflows into usable AI products
Help establish technical patterns and engineering standards for applied AI across the wider organization
What You’ll Bring
Strong software engineering experience across areas such as backend engineering, distributed systems, infrastructure, developer platforms, or ML systems
Experience shipping AI or ML systems into production rather than working exclusively on prototypes or research
Practical experience with one or more of LLMs, RAG, NLP, AI agents, model evaluation, observability, data ingestion, or AI/ML infrastructure
Strong understanding of software architecture, system design, reliability, and production engineering
Ability to operate comfortably across both AI-specific problems and traditional software engineering challenges
A high bar for engineering quality, including the ability to critically review AI-generated code rather than treating generated output as production-ready
A pragmatic builder mentality and an appetite for high ownership in a fast-moving technical environment
Python
LLMs / Generative AI
Retrieval-Augmented Generation (RAG)
Embeddings & semantic retrieval
LLM evaluation frameworks
AI observability & monitoring
Workflow orchestration
AI / ML infrastructure
Distributed backend systems
Why Join? This is an opportunity to join at an important inflection point: the company has strong funding, an established product and customer base, and executive-level commitment to making AI a fundamental part of the platform.
You’ll have significant ownership over the infrastructure that determines whether AI systems actually work in production, from how information enters the system and is retrieved, through to how outputs are evaluated, monitored, and continuously improved.
About People In AI People In AI is a specialist recruitment partner dedicated to connecting exceptional AI, Machine Learning, Data, and Software Engineering talent with some of the most ambitious technology companies in the market.
We work closely with technical founders and engineering leaders to identify opportunities where talented engineers can have genuine impact, combining deep technical understanding with a highly personalized recruitment experience.
#J-18808-Ljbffr