Why This Job is Featured on The SaaS Jobs
This Research Engineer role sits at the intersection of applied AI research and product delivery, a combination increasingly central to modern SaaS offerings that differentiate through model capability rather than workflow alone. With Fastino working on specialized, efficient LLMs and maintaining widely adopted open source models, the position is relevant to the growing segment of SaaS companies building platform value on proprietary model performance, evaluation rigor, and reliable iteration cycles.
From a career standpoint, the work spans the full loop that many AI-first SaaS teams need: architecture experimentation, data pipeline design, alignment methods such as preference optimization, and evaluation design tied to real usage. That breadth builds durable SaaS-adjacent research engineering skills, especially the discipline of turning research outcomes into repeatable improvements that can be shipped to customers without sacrificing code health or documentation standards.
The role is best suited to someone who prefers ambiguous technical problem spaces and can balance independent research with collaborative engineering execution. It will fit professionals who want their model work to be judged by measurable quality and deployment readiness, and who are motivated by contributing to both internal roadmaps and externally visible artifacts such as open source releases.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Research Engineer - Large Language Models
Full-time | Hybrid or Remote | Reports to Founders
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:
- Experiment with novel language model architectures, helping drive and execute Fastino's research roadmap
- Optimize Fastino’s multimodal models to improve response quality, instruction adherence, and overall performance metrics
- Architect data processing pipelines, implementing filtering, balancing, and captioning systems to ensure training data quality across diverse content categories
- Implement reinforcement learning techniques including Direct Preference Optimization and Generalized Reward Preference Optimization to align model outputs with human preferences and quality standards
- Build robust and real-world motivated evaluations
- Partner with Fastino engineering team to ship model updates directly to customers
- Establish best practices for code health and documentation on the team, to facilitate collaboration and reliable development
You Should Have:
- Advanced degree (Master's or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline with concentrated study in deep learning and computer vision methodologies
- Demonstrated ability to do independent research in Academic or Industry settings
- Substantial industry experience in large-scale deep learning model training, with demonstrated expertise in at least one of Large Language Models, Vision-Language Models, Diffusion Models, or comparable generative AI architectures
- Comprehensive technical proficiency and practical experience with leading deep learning frameworks, including advanced competency in one of PyTorch, JAX, TensorFlow, or equivalent platforms for model development and optimization
