AI Platform Engineer
fastino.ai
San Francisco Bay Area, CARemoteFull Time
Job Description
AI Platform Engineer
Full-time | Hybrid or Remote
Introduction:
Join us at Fastino as we build the next generation of LLMs. Our team, boasting alumni from Google Research, Apple, Stanford, and Cambridge is on a mission to develop specialized, efficient AI.
Fastino's GLiNER family of open source models has been downloaded more than 5 million times and is used by companies such as NVIDIA, Meta, and Airbnb
Fastino has raised $25M (as featured in TechCrunch) through our seed round and is backed by leading investors including Microsoft, Khosla Ventures, Insight Partners, Github CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.
The Role:
We are looking for a systems-level engineer to own Fastino’s model platform end-to-end.
This is not a feature role.
You will design and build:
Training pipelines
Fine-tuning workflows
RL infrastructure
Data ingestion and curation systems
Inference services
Scalability and backend architecture
You will own the platform that turns models into production systems.
What You’ll Work On:
Architect distributed fine-tuning pipelines for small encoder and decoder models
Implement LoRA, adapters, distillation, and compression workflows
Design experiment tracking, reproducibility, and dataset versioning systems
Optimize training efficiency (GPU utilization, memory, throughput, cost)
Design scalable RL training workflows (policy optimization, reward modeling)
Integrate RL with supervised fine-tuning and distillation
Build evaluation loops and automated regression detection
Build scalable ingestion pipelines for structured and unstructured data
Design dataset curation, filtering, and quality enforcement systems
Implement reproducible data workflows tied to training runs
Architect low-latency inference services
Design safe production deployment workflows
Strong candidates will have:
Deep experience with PyTorch and transformer architectures
Experience building production ML systems end-to-end
Experience with distributed training and inference
Experience optimizing GPU workloads
Strong backend and systems engineering fundamentals
Experience with containerization and orchestration
Cloud infrastructure experience (AWS/GCP/Modal/Together.ai etc)
Bonus:
Experience with RL or RLHF
Experience with distillation and compression
Experience building internal ML platforms
Full-time | Hybrid or Remote
Introduction:
Join us at Fastino as we build the next generation of LLMs. Our team, boasting alumni from Google Research, Apple, Stanford, and Cambridge is on a mission to develop specialized, efficient AI.
Fastino's GLiNER family of open source models has been downloaded more than 5 million times and is used by companies such as NVIDIA, Meta, and Airbnb
Fastino has raised $25M (as featured in TechCrunch) through our seed round and is backed by leading investors including Microsoft, Khosla Ventures, Insight Partners, Github CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.
The Role:
We are looking for a systems-level engineer to own Fastino’s model platform end-to-end.
This is not a feature role.
You will design and build:
Training pipelines
Fine-tuning workflows
RL infrastructure
Data ingestion and curation systems
Inference services
Scalability and backend architecture
You will own the platform that turns models into production systems.
What You’ll Work On:
Architect distributed fine-tuning pipelines for small encoder and decoder models
Implement LoRA, adapters, distillation, and compression workflows
Design experiment tracking, reproducibility, and dataset versioning systems
Optimize training efficiency (GPU utilization, memory, throughput, cost)
Design scalable RL training workflows (policy optimization, reward modeling)
Integrate RL with supervised fine-tuning and distillation
Build evaluation loops and automated regression detection
Build scalable ingestion pipelines for structured and unstructured data
Design dataset curation, filtering, and quality enforcement systems
Implement reproducible data workflows tied to training runs
Architect low-latency inference services
Design safe production deployment workflows
Strong candidates will have:
Deep experience with PyTorch and transformer architectures
Experience building production ML systems end-to-end
Experience with distributed training and inference
Experience optimizing GPU workloads
Strong backend and systems engineering fundamentals
Experience with containerization and orchestration
Cloud infrastructure experience (AWS/GCP/Modal/Together.ai etc)
Bonus:
Experience with RL or RLHF
Experience with distillation and compression
Experience building internal ML platforms