Machine Learning Engineer

10a Labs; New York (NY); Remote; USD 150,000 - 250,000; Full time

Job summary

A cutting-edge AI security firm in New York is seeking a Machine Learning Engineer to build and deploy high-performance ML systems focused on abuse detection. This role requires 3-8 years of experience with traditional ML and LLMs, and offers a competitive salary range of $150K–$250K alongside generous benefits. Work is fully remote for U.S.-based candidates.

Benefits

Performance-based annual bonus

Generous PTO and paid holiday schedule

401(k) plan

Support for continuing education

Qualifications

• 3-8 years of experience building and deploying ML systems in production.
• Strong foundation in traditional ML techniques.
• Hands-on experience with LLMs, including fine-tuning.

Responsibilities

• Build and deploy classification systems with high throughput.
• Integrate feedback loops to refine model performance.
• Collaborate with teams to integrate ML components into production systems.

Skills

Machine learning systems

LLMs

Python

Data processing

Model evaluation

Tools

AWS

GCP

OpenAI

Hugging Face

Job description

About 10a Labs: 10a Labs is an applied research and AI security company trusted by AI unicorns, Fortune 10 companies, and U.S. tech leaders. We combine proprietary technology, deep expertise, and multilingual threat intelligence to detect abuse at scale. We also deliver state-of-the-art red teaming across high-impact security and safety challenges.

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.

3–8 Years of Industry Experience | Remote | High-Impact

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.
• 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:

• 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.
• 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.
• Time Off: Generous PTO and paid holiday schedule.
• Retirement: 401(k) plan.