Machine Learning Engineer
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Job Summary
This is a remote position.
Type: Freelance / Contract
Location: Fully Remote
Hours: Around 20 hours/week with flexible scheduling
Process: Short screening call Technical assessment Onboarding
DevFixr is recruiting Machine Learning Engineers for a client building training environments for frontier AI labs. Youll work on challenging ML engineering tasks designed to train and evaluate advanced AI models.
What Youll Do
- Design realistic and challenging ML engineering coding tasks based on real-world codebases.
- Build working reference solutions for each task.
- Create automated tests to verify that solutions are correct and robust.
- Ensure tasks are clearly defined and cannot be solved through shortcuts.
- Improve tasks based on reviewer feedback.
What Were Looking For
- 4 years of hands-on ML Engineering experience in Python.
- Candidates with 3 years of relevant Python/ML experience may also be considered when their education and practical experience demonstrate strong technical capability.
- Experience training or fine-tuning models using PyTorch JAX TensorFlow or similar frameworks.
- Strong Python coding debugging testing and experience working with large or multi-file codebases.
- Strong written English and ability to write clear technical specifications.
Nice to Have
- GPU/ML systems experience: CUDA Triton FSDP DeepSpeed kernel optimization multi-GPU or distributed training.
- Experience with AI training/evaluation benchmarks model evaluations or RL environments.
- Kaggle competitive programming or open-source ML contributions.
Required Skills:
What the client is looking for Real engineering depth. The kind that comes from shipping and maintaining production systems. Years on a CV matter less than the quality of your judgement; some of the clients strongest contributors are very young. Python or a very good reason not to. Python is the primary environment. But the client is language agnostic: if you have spent a career in Java C or COBOL and never picked up Python that expertise is genuinely valuable there is real demand for tasks that move legacy code into modern languages. Intellectual honesty. You say so when you dont know something you raise problems while they are still fixable and when you say youre nearly done youre nearly done. This matters more than almost anything else. Clear written English. Nearly everything you produce is written specification that someone else has to be able to read without asking you a question.
