Machine Learning Engineer (Remote)

VaultKey Global Recruitment; India; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

VaultKey Global Recruitment seeks a highly skilled Machine Learning Expert to contribute to an AI training project, focusing on model development, training and inference systems, numerical computing, performance optimization, and Python.

The role requires advanced ML expertise, hands-on Python proficiency, and meaningful experience with at least two ML frameworks. You will design, validate, and optimize end-to-end ML workflows, explain decisions, and document engineering trade-offs.

Qualifications

• Master’s or PhD in a quantitative field with ML focus.
• Strong professional or research ML experience.
• Proficiency in Python and ML tooling.
• Experience with at least two ML frameworks or inference tools.
• Ability to debug ML systems beyond API usage.
• Ability to explain implementation choices and trade-offs clearly.
• Experience building reproducible ML workflows.

Responsibilities

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible programmatic workflows using Python and command-line tools.
• Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation.
• Optimize training or inference for latency, throughput, memory usage, and hardware utilization.
• Diagnose numerical instability, memory bottlenecks, and distributed-system failures.
• Compare model implementations and verify results for correctness and reproducibility.
• Review AI-generated code and technical solutions for quality and efficiency.
• Design objective tests, benchmarks, and verification criteria.
• Clearly document technical decisions, trade-offs, and limitations.

Skills

Machine Learning experience

Python proficiency

ML frameworks experience

Model training & evaluation

Debug ML systems

Explain decisions & trade-offs

Reproducible workflows

Education

Master’s or PhD in CS/ML/AI/Applied Math/Statistics/Engineering

Job description

Schedule: Flexible and autonomous, you pick the hours and days (including weekends if desired)

micro1 is looking for highly skilled Machine Learning Experts to contribute to an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python.

This role is designed for experienced ML engineers and researchers who understand the systems beneath high-level APIs. Candidates should have meaningful practical experience with multiple tools from the modern ML stack and be able to explain what they personally built, optimized, or operated.

What You’ll Work On

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible programmatic workflows using Python and command-line tools.
• Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation.
• Optimize training or inference for latency, throughput, memory usage, and hardware utilization.
• Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions.
• Compare model implementations and determine whether results are correct and reproducible.
• Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality.
• Design objective tests, benchmarks, and verification criteria.
• Clearly document technical decisions, trade-offs, and limitations.

Required Qualifications

• A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python.
• Meaningful experience with at least two relevant ML frameworks, libraries, or inference tools.
• Strong understanding of model training, evaluation, numerical computation, or inference.
• Ability to debug ML systems beyond surface-level API usage.
• Ability to explain implementation decisions, performance trade-offs, and failure modes clearly.
• Experience building reproducible technical workflows.