Machine Learning Engineer
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- Work arrangement
- onsite
- Employment
- FullTime
- Stated compensation
- USD 180,000–230,000 1 YEAR
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Requirements
2 with evidence7 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
From employer posting metadata - Degree requirement
- Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD optional, not required)
“Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD optional, not required)”
See matched passage in original description ↓ - Employment type
- FullTime
- Compensation
- USD 180,000–230,000 1 YEAR
7 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Security clearance
- Not mentioned
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- Not specified
- Citizenship requirement
- Not mentioned
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- Not specified
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Original job description
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Job Description
We’re looking for a Machine Learning Engineer to build and deploy production-grade AI systems. In this role, you’ll take models from research to real-world applications, designing, optimizing, and scaling systems that power critical workflows across the enterprise.
You’ll work closely with research, product, and engineering teams to turn cutting-edge capabilities into reliable, high-performance systems in production.
Key Responsibilities
Model Development & Deployment: Build, fine-tune, and deploy machine learning models into production environments
Systems Engineering: Design scalable pipelines for training, inference, evaluation, and monitoring
Performance Optimization: Improve latency, throughput, cost efficiency, and reliability of ML systems
Data & Infrastructure: Work with large-scale datasets and integrate models with internal systems and APIs
Cross-Functional Collaboration: Partner with product and engineering teams to deliver end-to-end AI features
Evaluation & Monitoring: Implement robust evaluation frameworks, observability, and feedback loops
Minimum Qualifications
Education: Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD optional, not required)
Technical Skills: Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
Production Experience: Experience deploying and maintaining ML systems in production environments
Systems Knowledge: Familiarity with distributed systems, data pipelines, and cloud infrastructure (e.g., AWS, GCP)
Practical ML Expertise: Experience with model training, fine-tuning, evaluation, and iteration at scale
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