AI Engineer at Fastino in Remote

Full-time | 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. WHAT YOU’LL WORK ON • Innovate at the edge of efficiency by designing and deploying high-performance agentic systems that leverage Fastino’s optimized model architectures to outperform traditional LLM benchmarks. • Bridge the gap between research and production by collaborating with engineering teams to turn novel architectural breakthroughs into scalable, low-latency solutions for enterprise customers. • Drive rapid, iterative prototyping of AI functionalities, refining model performance and task-accuracy based on real-world telemetry to ensure specialized models meet rigorous developer standards. • Own the stability and throughput of inference pipelines, proactively solving scalability bottlenecks to ensure models deliver consistent, reliable performance under massive operational loads. • Architect large-scale data and fine-tuning strategies to continuously improve the precision and domain-specific reliability of the Fastino models. What We’re Looking For: • Required:2+ years of hands-on experience in AI/ML engineering roles • Required: Demonstrated proficiency with LLMs and a track record of applying AI/ML techniques to solve complex, unstructured problems • Required: You are comfortable working across the stack from prompt engineering and vector DB tuning to Kubernetes deployment and API design. • Optional: Experience building microservices that handle high-concurrency agentic workloads. • Optional: Familiarity with GLiNER or other information extraction architectures.