About the Role
A Series A AI/ML startup focused on model training and evaluation is looking for a Founding ML Engineer to build and scale core machine learning systems from the ground up. This is a hands-on, high-impact role for someone who thrives at the intersection of research intuition and engineering execution. You will work closely with the founding team to design, train, and ship production-grade ML models while helping set the foundation for technical culture, infrastructure, and research practices.
What You'll Do
- Build and optimize end-to-end ML pipelines, from data ingestion to deployment.
- Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.
- Develop efficient training and inference systems leveraging distributed compute.
- Partner with data and product teams to translate ideas into measurable ML impact.
- Contribute to model monitoring, evaluation, and continual learning frameworks.
- Establish best practices in model versioning, reproducibility, and scalability.
What We're Looking For
- 3–10 years of hands-on experience as an ML Engineer, Applied Scientist, or Research Engineer.
- Proficiency in Python and at least one of PyTorch, TensorFlow, or JAX.
- Experience building production-grade end-to-end ML pipelines (data ingestion, training, deployment).
- Experience implementing and fine-tuning LLMs, embeddings, and generative models for real-world applications.
- Hands-on experience with distributed training/inference and scalable ML systems.
- Experience with cloud platforms (AWS, GCP, or Azure) and ML tooling (MLflow, Weights & Biases) for experimentation and model tracking.
- Bias for action, autonomy, and eagerness to shape something from scratch.
Compensation
$220,000 – $300,000 USD annually, commensurate with experience.