10Alabs~1mo ago

Machine Learning Engineer

United States·New Yorkmid

Data ScienceMachine Learning EngineerDataData & AI

10 views0 saves0 applied

Apply Now

Quick Summary

Requirements Summary

At least 3–8+ years of professional working experience as a Machine Learning engineer, building, owning and deploying machine learning systems in production.

Technical Tools

Data ScienceMachine Learning EngineerDataData & AI

About the Role

~1 min read

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.

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

~1 min read

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

~1 min read

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.

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.