About 10a Labs:
10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.
About The role:
We’re looking for an experienced ML engineer with a strong foundation in traditional ML and hands-on experience applying those skills to modern LLM systems. This is an applied role for someone who owns the full ML lifecycle—from data pipelines and model training to evaluation, deployment, and ongoing iteration in real-world production environments.
At least 3–8+ Years of Industry Experience Required
In This Role, You Will
- 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.
We’re Looking For Someone Who
- Has designed and deployed full ML pipelines (data ingestion → model training → evaluation → deployment → feedback).
- Comfortable working with noisy or adversarial real-world data, not just clean benchmarks.
- Understands the performance tradeoffs between recall, precision, latency, and cost—and knows how to tune for impact.
- Moves fast with strong instincts for where to prototype, where to systematize, and how to deliver models that hold up in production.
- Brings curiosity, creativity, innovation, and a bias for action in ambiguous environments.
Requirements
- At least 3–8+ years of professional working experience as a Machine Learning engineer, building, owning and deploying machine learning systems in production.
- 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.
- Clear communicator of complex technical concepts to non-technical audiences.
- Experience deploying models in cloud environments (e.g., AWS, GCP).
- Experience designing or integrating human-in-the-loop systems for model evaluation or policy alignment.
Nice To Have Experience With
- Real-time ML pipelines.
- Scaled moderation or large-scale threat detection.
- Vision, audio, OCR, or deepfake classification.
- Designing multilingual embedding systems with code-switch detection.
- Agentic pipelines for explainable or rationale-based moderation.
- Rapid prototyping using modern LLM APIs and frameworks (e.g., OpenAI, Hugging Face, LangChain).
- Error analysis and model forensics—comfortable diving into false positives and failure modes.
What Success Looks Like In The First 3 Months
- 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.
- You've contributed meaningful ideas to data strategy—synthetic generation, clustering schema, or policy alignment tuning.
- You’ve owned a full subsystem—from ideation to deployment—and seen it hold up under real usage and scrutiny.
Compensation & Benefits
- Salary Range: $150K–$250K, depending on professional experience, location, and other factors.
- Bonus: Performance-based annual bonus.
- Professional Development: Support for continuing education, conferences, or training.
- Work Environment: Fully remote, U.S.-based.
- Health Benefits: Comprehensive health, dental, and vision coverage.
- Time Off: Generous PTO and paid holiday schedule.
- Retirement: 401(k) plan.