10a Labs

Machine Learning Engineer

10a Labs · New York, NY

  • Hybrid
  • Full-time
  • $200,000 / year
  • New York, NY

Job highlights

  • Build and deploy advanced ML systems.
  • Own the full ML lifecycle in production.
  • Work with LLMs and traditional ML.
  • Collaborate on cutting-edge AI safety.
  • Fully remote U.S.-based position.

About the role

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.

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.

Key Skills/Competency

  • Machine Learning Engineering
  • LLM Systems
  • ML Pipelines
  • Model Deployment
  • Python
  • Data Ingestion
  • Model Training
  • Evaluation
  • Cloud Environments
  • Human-in-the-Loop Systems

Skills & topics

  • Machine Learning Engineer
  • ML Engineer
  • LLM
  • Python
  • AI
  • Deep Learning
  • Data Science
  • Cloud Computing
  • AWS
  • GCP
  • NLP

How to get hired

  • Tailor your resume: Highlight experience with full ML pipelines, LLMs, and production deployment. Quantify achievements in ML engineering.
  • Showcase your skills: Emphasize Python proficiency, cloud deployment (AWS/GCP), and experience with LLM frameworks.
  • Demonstrate understanding: Articulate your knowledge of ML tradeoffs (recall, precision, latency) and experience with adversarial data.
  • Prepare for interviews: Be ready to discuss your experience with ML system design, troubleshooting, and collaboration.
  • Research 10a Labs: Understand their mission in AI safety and threat intelligence to align your answers with their goals.

Technical preparation

Master Python and ML/NLP libraries.,Practice LLM fine-tuning and prompt engineering.,Build end-to-end ML pipelines locally.,Deploy models on AWS or GCP.

Behavioral questions

Describe a challenging ML deployment.,How do you handle noisy data?,Explain ML tradeoffs to non-experts.,Share an innovative ML solution.

Frequently asked questions

What specific LLM experience is 10a Labs looking for in a Machine Learning Engineer?
10a Labs is seeking Machine Learning Engineers with hands-on experience in LLMs such as OpenAI, Claude, and LLaMA. This includes practical application in fine-tuning models and prompt engineering, as well as familiarity with text processing, semantic embeddings, and vector search.
How important is experience with traditional ML techniques for this Machine Learning Engineer role at 10a Labs?
A strong foundation in traditional ML techniques, including clustering, anomaly detection, and supervised learning, is crucial. This role requires applying these traditional methods to modern LLM systems, making a solid understanding of both essential for success.
What does 'owning the full ML lifecycle' mean for this Machine Learning Engineer position?
Owning the full ML lifecycle involves end-to-end responsibility for ML systems: from data ingestion and model training to evaluation, deployment, and continuous iteration based on real-world feedback and production performance.
Can you describe the typical work environment and team collaboration for a Machine Learning Engineer at 10a Labs?
This is a fully remote, U.S.-based position. You'll collaborate closely with researchers, SMEs, and a broader team of engineers to integrate ML components into production systems, ensuring robust, observable, and scalable solutions.
What kind of real-world data challenges can a Machine Learning Engineer expect at 10a Labs?
Machine Learning Engineers at 10a Labs should be comfortable working with noisy or adversarial real-world data, not just clean benchmarks. The role involves building systems that maintain high recall and precision under demanding conditions.
How does 10a Labs support professional development for their Machine Learning Engineers?
10a Labs offers support for continuing education, conferences, or training to help their Machine Learning Engineers stay at the forefront of the field and develop new skills.
What are the key performance indicators for a Machine Learning Engineer in their first 3 months at 10a Labs?
Within the first three months, success looks like deploying a functioning moderation system, refining a model evaluation pipeline, contributing to data strategy, and owning a full subsystem from ideation to deployment.