Remote ML Engineer — End-to-End, AI-Driven Systems

Careerminds; United States; Remote; USD 140,000 - 220,000; Full time

Job summary

Careerminds is seeking a Machine Learning Engineer to own end-to-end ML products—from problem framing to production deployment and measurable outcomes. You will work in a 100% remote role with a small product strategy team and autonomous ownership across discovery, design, build, and ship phases.

You should have deep experience shipping ML systems, with proficiency in Python, AWS, and modern ML frameworks. Strong track record with LLMs, agents, and end-to-end pipelines is essential.

Qualifications

• 5+ years shipping ML systems into production with measurable impact.
• Deep knowledge of classical ML and deep learning (PyTorch or TensorFlow).
• Experience deploying LLMs in production with retrieval and evals.
• Proficient with agentic coding tools (Claude Code/Design) and real-world deployments.
• Strong software fundamentals: Python, Git, cloud (AWS), containers.

Responsibilities

• Canon datasets for titles, companies, skills, and industries; manage scalable ingestion.
• Design rules-based resolution pipelines with LLМ escalation and invariant checks.
• Build nightly agent loops to adjudicate ambiguous entities and gates.
• Develop two-tower retrieval, cross-encoder reranking for job matching.
• Create mobility embeddings from career sequences and real-world signals.
• Fine-tune models against outcome labels and deploy agentic systems.
• Evaluate with rigorous metrics and ensure GDPR/EU AI Act considerations.

Skills

Production ML

Python

Git

AWS

LLMs in production

Claude Code

Claude Design

PyTorch

Tools

Claude Code

Claude Design

PyTorch

TensorFlow

AWS

Job description

Careerminds is seeking a Machine Learning Engineer to own end-to-end ML products—from problem framing to production deployment and measurable outcomes. You will work in a 100% remote role with a small product strategy team and autonomous ownership across discovery, design, build, and ship phases.

You should have deep experience shipping ML systems, with proficiency in Python, AWS, and modern ML frameworks. Strong track record with LLMs, agents, and end-to-end pipelines is essential.