Senior Machine Learning

Senior Machine Learning / AI Engineer – Deep Learning & GenAI (Expert-Level)

Location: Hybrid- DHA Phase 6 Lahore

Level: Principal / Staff / Senior

Experience: 4+ years hands-on (Minimum 3+ years in GenAI & Transformers)

TL;DR — If you're not fluent in the architecture of GPT-style models, haven't trained a multi-billion parameter model, or can't debug CUDA kernel failures, this role is not for you.

About the Role

We are seeking an elite ML/AI Engineer with deep theoretical and engineering mastery in deep learning, LLMs, and generative architectures. You must have trained, fine-tuned, benchmarked, and deployed transformer-based models at scale, understand the research behind every paper you cite, and have shipped production-grade ML systems across distributed GPU environments.

This is a zero-to-one role: no prebuilt dataset, no precleaned labels, no off-the-shelf pipelines. Just research, code, and compute.

You will be responsible for:

• Designing and training foundation models (e.g., GPT, T5, Mistral, LLaMA) from scratch on multi-node GPU clusters.
• Leading full-stack GenAI architecture: tokenizer design, attention variants, pretraining schemes, alignment (RLHF), quantization, serving.
• Conducting frontier research in model optimization, mixture-of-experts (MoE), context length extrapolation, prompt tuning, and memory efficiency.
• Implementing and extending SOTA methods from arXiv (e.g., Phi-3, Gemini, FlashAttention-2, GQA, LLaVA, Orca, DPO, ZeRO-Infinity).
• Managing model lifecycle: data curation → pretraining → finetuning → evaluation → quantization → deployment → postmortems.
• Working with multi-modal inputs (text, image, video, audio, embeddings) for cross-domain GenAI systems.
• Driving production-grade optimization: low-latency inference, batching strategies, CUDA kernel debugging, memory offloading, model sharding.

Required Expertise (No Exceptions):

Core ML/DL:

• Transformers, self-attention, residual connections, GELUs, normalization strategies (RMSNorm, LayerNorm, etc.)
• LLM scaling laws, curriculum learning, token sampling strategies (Top-k, Top-p, Temperature, Mirostat, nucleus filtering)
• Contrastive learning, masked modeling, autoregressive generation, denoising diffusion Training Infrastructure:
• PyTorch Lightning, DeepSpeed, HuggingFace Accelerate, Fully Sharded Data Parallel (FSDP), ZeRO-3
• GPU/TPU cluster management, distributed checkpointing, mixed precision (fp16, bfloat16), quantization-aware training
• Data streaming at scale with WebDataset, TFRecord, Parquet

Model Optimization & Serving:

• Quantization: GPTQ, AWQ, SmoothQuant, LLM.int8(), QLoRA
• Compilers: TensorRT, Torch-TensorRT, TVM, ONNX Runtime, XLA, GGUF
• Model serving: Triton Inference Server, vLLM, TGI, HuggingFace Text Generation Inference

GenAI Systems:

• RLHF pipelines: reward modeling, PPO, DPO, ORPO, RLAIF
• Retrieval-Augmented Generation (RAG): hybrid semantic search, vector DB integration, prompt composition
• Tokenizer development: SentencePiece, Tiktoken, Byte Pair Encoding (BPE), Unigram LM

Software Engineering:

• Clean, modular, testable Python code with CI/CD pipelines
• Profiling tools: PyTorch Profiler, Nsight, nvtop, nvprof, memory profiler
• Containerization: Docker, NVIDIA Container Toolkit, Kubernetes for distributed training

Mathematical Rigor:

• Optimization: AdamW, Lion, RMSProp, gradient clipping, learning rate schedulers
• Loss functions: Cross-entropy, KL divergence, cosine similarity, contrastive losses
• Strong command of linear algebra, probability, statistics, and numerical methods

Job Type: Full-time

Work Location: In person

Application Deadline: 20/08/2025

Expected Start Date: 25/08/2025