Machine Learning Engineer
5 days ago
Machine Learning Expert
Remote | Part-Time | $75–$125 per hour
Role Overview
Join a collaborative project with one of the
world's leading AI research labs
as a
Machine Learning Expert
. This role involves contributing technical expertise to the
design, development, and refinement of state-of-the-art machine learning models
. Responsibilities include building scalable data pipelines, evaluating AI-generated outputs, and helping shape systems that replicate real-world engineering workflows.
Key Responsibilities
- Design, build, and optimize
machine learning models
and
data processing pipelines
. - Evaluate AI-generated outputs for
accuracy, performance, and reliability
. - Collaborate with researchers and engineers to improve
model architectures, training strategies, and deployment methods
. - Contribute to creating systems that enhance real-world AI-driven workflows.
Ideal Qualifications
- Bachelor's or Master's degree in
Computer Science, Machine Learning, Data Science
, or related field (
PhD preferred
). - Strong expertise in
machine learning techniques
and
deep learning frameworks
such as
TensorFlow
or
PyTorch
. - Proficiency in
Python
and experience working with
large-scale data processing systems
. - Excellent
problem-solving and analytical skills
. - Prior experience in research, engineering, or published work in the machine learning field is a plus.
Project Details
- Start Date:
Immediate - Duration:
Minimum
1–2 months - Commitment:
~20 hours/week (part-time) with potential to extend up to
40 hours/week - Schedule:
Fully remote and asynchronous — flexible working hours
Compensation & Contract
- Hourly Rate:
$75–$125 per hour (based on experience) - Performance Bonus:
$20–$100/hr for top performers - Status:
Independent Contractor - Payments:
Weekly via
Stripe Connect
Application & Onboarding Process
- Submit your
resume and application form
. - Complete a
short AI interview
(~15 minutes) to discuss your background and expertise. - Participate in a
paid work trial
(~3 hours) to assess your ability to design and evaluate domain-specific outputs.
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