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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in Machine Learning Engineering with a strong focus on Python coding, debugging, and testing. Proficient in training and fine-tuning models using frameworks like PyTorch, JAX, and TensorFlow, while also capable of creating clear technical specifications and automated tests.
Highest-signal resume keywords
Machine Learning EngineeringPython ProgrammingModel Training and Fine-TuningAutomated TestingTechnical Specification Writing
ATS Keywords
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Hard Skills
Machine Learning EngineeringPython ProgrammingModel TrainingModel Fine-TuningDebuggingTestingLarge Codebase ManagementTechnical Specification Writing
Soft Skills
Strong Written English
Tools & Technologies
PyTorchJAXTensorFlowCUDATritonFSDPDeepSpeed
Industry Keywords
AI TrainingModel EvaluationRL EnvironmentsKaggleCompetitive ProgrammingOpen-Source Contributions
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- 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
- Complete a short screening call and technical assessment before onboarding
Requirements
What you’ll need- 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, and testing skills
- Experience working with large or multi-file codebases
- Strong written English
- Ability to write clear technical specifications
- Nice to have: GPU/ML systems experience with CUDA, Triton, FSDP, DeepSpeed, kernel optimization, multi-GPU, or distributed training
- Nice to have: Experience with AI training/evaluation, benchmarks, model evaluations, or RL environments
- Nice to have: Kaggle, competitive programming, or open-source ML contributions
Benefits
Comp & perks- Remote position
- Fully remote work arrangement
- Around 20 hours/week with flexible scheduling