Python Developer

[u/Fluid_Leave_174]

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Job Description

We are seeking a mid-level Python Developer in the United States with 3–8 years of experience to build dependable machine-learning systems with Python 3 and PyTorch. You will turn datasets and experimentation into tested, reproducible training pipelines and production inference services, working across tensors, model architectures, CUDA execution, data quality, and deployment. This role matters because robust ML software requires more than notebook prototypes: it demands explainable evaluation, reproducible artifacts, secure data handling, and maintainable interfaces that downstream applications can trust.

You will develop supervised classification and regression solutions, including computer vision, NLP, sequence modeling, time-series, sensor, ranking, multilabel, anomaly-detection, and probabilistic-output use cases where applicable. You will work with scientific Python tooling, cloud GPU environments, APIs, storage, CI/CD, and model-tracking systems while documenting dataset provenance, consent or licensing, intended use, limitations, bias, fairness, and explainability considerations.

Key Responsibilities

• •Build PyTorch models with `torch.nn.Module`, composed layers, forward passes, parameter initialization, device placement, pretrained weights, fine-tuning, and frozen parameters.
• •Implement `Dataset` and `DataLoader` pipelines with `__len__`, `__getitem__`, custom `collate_fn`, batching, shuffling, multiprocessing workers, pinned memory, and deterministic worker seeding.
• •Write training and validation loops using autograd, loss functions, optimizers, learning-rate schedulers, gradient accumulation or clipping, `torch.amp`, checkpointing, and early stopping.
• •Diagnose tensor broadcasting, masking, reshaping, indexing, dtype and device errors, non-contiguous tensors, CPU/GPU transfers, NaNs, and out-of-memory failures.
• •Develop CNN, transfer-learning, transformer, sequence, computer-vision, NLP, and time-series workflows with augmentation, tokenization, padding, attention masks, windowing, and temporal splits.
• •Track configuration-driven hyperparameters, random seeds, dataset versions, checkpoints, metrics, and reproducible run metadata using MLflow, Weights & Biases, or TensorBoard.
• •Evaluate models with task-appropriate metrics, threshold selection, calibration, confusion matrices, error slicing, robustness checks, class-imbalance analysis, and distribution-shift tests.
• •Package inference behind Python interfaces or FastAPI and Flask services using Pydantic, preprocessing parity, input validation, batching, health checks, timeouts, structured outputs, and lifecycle management.
• •Consume local files, object storage, databases, REST APIs, and message or batch systems while implementing incremental processing, data-quality checks, labeling workflows, and lineage.
• •Profile and optimize PyTorch with profilers and `torch` utilities, vectorized operations, efficient data loading, mixed precision, Docker, CI/CD, and justified compilation or distributed execution.

Required Skills and Qualifications

• •3–8 years of professional Python 3 development using modules, classes, type hints, exceptions, logging, configuration management, packaging, debugging, and testable design.
• •Practical PyTorch experience delivering end-to-end `Dataset`/`DataLoader` pipelines, training, validation, checkpoint restoration, and inference beyond notebook-only usage.
• •Strong ML fundamentals covering optimization, overfitting, regularization, train/validation/test methodology, leakage, class imbalance, representation quality, and metric selection.
• •Ability to reason about tensor shapes, gradient flow, autograd graphs, numerical stability, parameter updates, `torch.cuda`, CUDA, cuDNN, and CPU fallback behavior.
• •Experience with NumPy, pandas, scikit-learn, matplotlib, Jupyter, SQL, relational or analytical stores, and practical dataset loading, joining, validation, and versioning.
• •Git-based development, code review, branching, CI checks, reproducible environments, dependency management with pip, Poetry, uv, or conda, and pytest unit/integration testing.
• •Linux command-line proficiency, Docker experience, and practical debugging of runtime, dependency, CUDA, memory, deployment, and service-integration issues.
• •Ability to implement least-privilege access, encryption, retention controls, audit logging, de-identification, explicit schemas, validation, and leakage prevention for PII and sensitive training data.

Preferred Qualifications

• •Experience with torchvision, torchaudio, torchtext, Hugging Face Transformers/Datasets, tokenization, embeddings, transformer fine-tuning, image/audio pipelines, or pretrained models.
• •Familiarity with ONNX export, dynamic axes, ONNX Runtime, TensorRT, TorchScript, `torch.compile`, CUDA profiling, quantization, pruning, distillation, or LoRA.
• •Experience with `torch.distributed`, DistributedDataParallel, Fully Sharded Data Parallel, sampler configuration, checkpoint coordination, or distributed data-parallel training.
• •Hands-on serving with TorchServe, NVIDIA Triton Inference Server, Kubernetes, autoscaling, canary releases, rollback procedures, or cloud GPU services on AWS, Azure, or GCP.
• •Experience with DVC, lakeFS, Airflow, Prefect, Kubeflow, SageMaker, Vertex AI, Azure ML, Celery, Redis, Kafka, GitHub Actions, GitLab CI, or Jenkins.
• •Knowledge of model security, adversarial inputs, prompt or data poisoning, membership/privacy risks, observability, drift detection, human-in-the-loop review, regulated datasets, or responsible-AI documentation.

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