Job Description & How to Apply Below
Company Description Laser Labs is an AI and software venture studio that partners with small and medium-sized enterprises to integrate workflow automation into everyday operations. The company focuses on reducing manual, repetitive work so business owners can concentrate on strategic decisions and growth. By building tailored AI solutions, Laser Labs quietly powers back-office efficiency while clients stay in the spotlight with their customers.
Team members work closely with real-world businesses, seeing direct impact from the tools and systems they create. This environment offers opportunities to experiment with cutting-edge AI while solving practical, high-value problems.
Role Description As an AI/ML Engineer at Laser Labs, you will design, build, and deploy machine learning models that automate and optimize clients’ workflows. You will work on data pipelines, feature engineering, model training, evaluation, and iteration using real-world business datasets. Day-to-day responsibilities include implementing and refining algorithms, developing and testing neural network architectures and using various tools such as n8n to build customized workflow automations.
This is a contract-based role, fully remote.
Qualifications
Strong foundation in Computer Science and Algorithms, with the ability to design efficient, scalable solutions.
Entrepreneurial spirit is a must, with past start-up experience highly preferred
ability to use n8n is required and the engineer should be able to learn how to use the platform if not familiar
Hands-on experience with Neural Networks and Pattern Recognition techniques for real-world applications.
Ability to utilize various LLMs to get the job done.
Proficiency in at least one modern programming language commonly used in ML (e.g., Python) and standard ML frameworks (e.g., PyTorch, Tensor Flow, Jupyter or similar).
Experience building end-to-end ML pipelines, including data preprocessing, feature engineering, training, validation, and deployment.
Comfort working with structured and unstructured data, and familiarity with SQL and version control tools (e.g., Git).
Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.
Communicate complex technical ideas clearly to non-technical stakeholders.
Compensation is negotiable and based on experience.
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