Founding Engineer

This role is for one of the Weekday's clients

Min Experience: 3 years

Location: USA

JobType: full-time

We are seeking a Founding ML Engineer to build and scale core machine learning systems from the ground up. This is a highly hands-on opportunity for someone who thrives at the intersection of research and engineering, moving seamlessly from rapid experimentation to robust production deployment. You will work closely with the founding team to design, train, and ship production-grade machine learning models while laying the groundwork for technical culture, infrastructure, and research best practices. The role requires strong ownership, speed of execution, and the ability to translate cutting-edge ML advancements—especially in LLMs and generative AI—into scalable, real-world applications. If you are passionate about building intelligent systems from first principles and shaping the ML foundation of an early-stage company, this role offers significant impact and growth.

Requirements

Key Responsibilities

• Design, build, and optimize end-to-end ML pipelines from data ingestion to deployment
• Implement and fine-tune LLMs, embeddings, and generative models for practical applications
• Develop scalable training and inference systems using distributed computing frameworks
• Collaborate with data and product teams to translate business problems into measurable ML solutions
• Build robust model evaluation, monitoring, and continuous learning frameworks
• Establish best practices for model versioning, reproducibility, and experimentation tracking
• Optimize performance, latency, and scalability of ML systems in production
• Manage large datasets and ensure efficient data preprocessing and feature engineering
• Contribute to infrastructure decisions related to cloud ML platforms and MLOps tooling

What Makes You a Great Fit

• 3–7 years of experience as an ML Engineer, Applied Scientist, or Research Engineer
• Strong programming skills in Python and proficiency in PyTorch, TensorFlow, or JAX
• Solid understanding of machine learning fundamentals including data preprocessing, model training, and optimization
• Hands-on experience with LLMs, generative models, or advanced NLP systems
• Experience with distributed systems and cloud ML infrastructure (AWS, GCP, or Azure)
• Familiarity with MLOps tools such as Weights & Biases, MLflow, or similar platforms
• Comfortable working with large-scale datasets and high-throughput systems
• Strong problem-solving skills and ability to operate in fast-paced, ambiguous environments
• Ownership mindset with a passion for building scalable AI systems