Penlink is a technology company bringing clarity to complex data for people who need it now. We partner with law enforcement agencies across the United States, offering a software solution to manage data and aid investigators solving crimes. It sounds like a lot of data and analytics, but really, it’s about improving the world and keeping safe the places we call home.
We focus on creating products that positively impact our communities and being "in the mission" and less about the laidback culture and amazing benefits – even though we offer those too. With our get it done attitude and focused mission we are growing at an unprecedented rate and are therefore seeking an AI Engineer to design, build, and ship AI-powered features in production. You’ll work across the stack from prompt design and model integration to evaluation, deployment, and optimization. You’ll partner closely with product, design, and engineering to turn business needs into reliable AI systems.
This role is a great fit if you enjoy working at the intersection of applied machine learning and product engineering, and you’re comfortable iterating in a space where the tools and best practices are still evolving quickly.
YOUR RESPONSIBILITIES
Design, develop, and implement AI-powered features and applications
Research, fine-tune, and deploy machine learning and large language models to support business and product initiatives
Integrate AI models into existing applications and backend systems through APIs and scalable architectures
Build and maintain data pipelines, including collecting, cleaning, preparing, and validating datasets for model training and evaluation
Monitor, evaluate, and maintain model performance through testing, benchmarking, and performance metrics
Optimize AI systems for scalability, latency, reliability, and cost efficiency
Collaborate cross-functionally with Product, Design, and Engineering teams to translate business needs into practical AI-driven solutions
Contribute to continuous improvement efforts surrounding AI development processes, tooling, and best practices
Communicate AI capabilities, limitations, and recommendations clearly to both technical and non-technical stakeholders
Promote responsible AI practices by considering model bias, ethical implications, and system transparency throughout development