10a Labs is looking for an ML engineer to own the full ML lifecycle for modern LLM systems, from data pipelines and model training to evaluation, deployment, and ongoing iteration in real-world production environments, to detect abuse at scale and deliver state-of-the-art red teaming.
Requirements
- Has designed and deployed full ML pipelines (data ingestion → model training → evaluation → deployment → feedback).
- Strong foundation in traditional ML techniques (e.g., clustering, anomaly detection, supervised learning).
- Hands-on experience with LLMs (e.g., OpenAI, Claude, LLaMA), including fine-tuning and prompt engineering.
- Proficiency in Python and modern ML / NLP tooling.
- Experience training models on small datasets and using in-context learning techniques.
- Familiarity with text processing pipelines, semantic embeddings, and vector search.
- Experience deploying models in cloud environments (e.g., AWS, GCP).
Responsibilities
- Build and deploy a multi-stage classification system optimized for high throughput and low latency, while ensuring high recall and precision.
- Integrate continuous feedback loops from human review to refine model performance.
- Design and implement real-world ML systems with a focus on robustness, observability, and scalability.
- Collaborate with researchers and SMEs to generate training data and test against edge cases.
- Work closely with a broader team of engineers to integrate ML components into production systems and ensure end-to-end system performance.
- You’ve designed and deployed a functioning moderation system using semantic embeddings and fine-tuned classifiers to detect abuse at scale.
- You've designed and refined at least one model evaluation pipeline, including precision / recall tracking and false positive analysis.
Other
- 3–8 Years of Industry Experience
- Remote
- High-Impact
- Clear communicator of complex technical concepts to non-technical audiences.
- Experience designing or integrating human-in-the-loop systems for model evaluation or policy alignment.
