Your Role
As an ML Engineer, you will focus on building, training, and optimizing the core models that power our AI agents. You’ll work on fine-tuning LLMs, training custom components, experimenting with model behavior, and pushing the boundaries of model performance and reliability.
You will work with Python, PyTorch, HuggingFace, LoRA/QLoRA, distributed training, evaluation suites, and data pipelines. You’ll help define how models adapt to customer data, how they perform across tasks, and how we scale our training workflows efficiently.
Your responsibilities will include:
- Fine-tuning and optimizing LLMs, embeddings models, and smaller neural components
- Designing and maintaining datasets, preprocessing pipelines, and training workflows
- Running experiments to evaluate model behavior, track improvements, and ensure reproducibility
- Benchmarking different models and architectures to guide product decisions
- Developing internal tooling to streamline training, evaluation, and data curation
- Working closely with the product and engineering teams to bring new ML capabilities to production
- Staying on top of the latest advances in LLM training, efficient finetuning, distillation, and evaluation
- and contributing your own expertise to continuously improve our ML stack and best practices ⚙️🧠
Ideal candidate
- 3–5+ years of experience in Machine Learning, Deep Learning, or Applied AI
- Strong background in Python, PyTorch, and modern ML tooling (HuggingFace, Weights & Biases, etc.)
- Hands-on experience fine-tuning LLMs or training neural models (even personal or research projects count!)
- Solid understanding of data preprocessing, training methodology, evaluation metrics, and experiment tracking
- Knowledge of efficient training techniques (LoRA, QLoRA, distillation, quantization, multi-GPU training, etc.)
- Passion for building high-quality, reliable ML systems with a strong product mindset
- Independent, rigorous, and proactive → we love people who bring ideas, challenge assumptions, and elevate the team