Job Title: Machine Learning Engineer

Company Name: 10Alabs

Job Url: https://job-boards.greenhouse.io/embed/job_app?for=10alabs&jr_id=6890b4ee4c7e851b90ac8e18&token=4000907009&utm_source=jobright

Job Description: 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: