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Company:
10a Labs
Posted: May 13, 2025
3-8 Years of Industry Experience | Remote | High-Impact
About The role: We're looking for an 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.
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:
- 3-8 years of experience building 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 experience and location.
- 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.
Read the full job description and apply online on the recuiter's web-site
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