Hire Deep Learning Engineersin 2 weeks
BearPlex deep learning engineers build production neural networks: model architecture design, distributed training, optimization, deployment. Specialists in PyTorch / JAX / Hugging Face for organizations that need deep learning beyond what frontier APIs provide.
What a Deep Learning Engineer actually does at BearPlex
A deep learning engineer at BearPlex specializes in production neural network engineering: architecture design (CNNs, Transformers, hybrid architectures), distributed training (multi-GPU, multi-node), optimization (quantization, distillation, pruning), and deployment (high-throughput inference, edge deployment, model serving infrastructure). They work across the full stack: PyTorch (primary), JAX, Hugging Face Transformers, ONNX Runtime, TensorRT, custom CUDA when needed. They've shipped: production deep learning models for computer vision (object detection, segmentation, OCR), NLP (classification, NER, custom Transformer architectures), audio (speech recognition, audio classification), multimodal (CLIP variants, vision-language models), and increasingly fine-tuned LLMs for specific domains. They're the right hire when frontier APIs can't solve the problem: when you need custom architecture, specialized models for your domain, or production infrastructure for self-hosted deep learning at scale.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Designed and trained custom Transformer architecture for a healthcare imaging client: model passed FDA SaMD review and is deployed across 8 hospital networks.
Built production inference infrastructure for a Series D AI startup: serves Llama 3.3 70B at 5K+ requests/sec with sub-200ms p95 latency.
Lead deep learning engineer for an industrial CV system: production-deployed across 12 manufacturing plants with edge inference at 30+ FPS on Jetson.
Optimized open-source LLM serving for a fintech client: through quantization + speculative decoding + serving optimization, cut inference cost 5× while maintaining quality.
Skills matrix
The capabilities every BearPlex Deep Learning Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| PyTorch production engineering | Expert | PyTorch 2.x · torch.compile · torch.distributed |
| JAX for research and production | Advanced | JAX · Flax · Equinox · TPU optimization |
| Distributed training (multi-GPU, multi-node) | Expert | DeepSpeed · FSDP · Megatron-LM · Colossal-AI |
| Custom architecture design | Expert | Transformer variants · CNN-Transformer hybrids · Custom attention patterns |
| Quantization (INT8, INT4, FP8) | Expert | GPTQ · AWQ · bitsandbytes · TensorRT INT8 |
| Knowledge distillation | Expert | Hugging Face Distillation · Custom distillation pipelines |
| Production inference optimization | Expert | vLLM · TensorRT-LLM · Triton Inference Server · ONNX Runtime |
| Edge deployment | Advanced | NVIDIA Jetson · Coral TPU · Core ML · TensorFlow Lite |
| GPU performance optimization | Expert | CUDA profiling · FlashAttention · Custom CUDA kernels when needed |
| Model architecture from research papers | Expert | Paper implementation · Hugging Face Transformers · Custom architecture coding |
| Production model serving at scale | Expert | Triton · TGI · vLLM · Multi-replica serving |
| Hardware-aware design (H100, A100, consumer GPUs) | Expert | GPU memory tuning · Hardware-specific optimizations |
How we vet deep learning engineers
Technical screen
60-minute deep-dive on past deep learning work. We probe: actual production models shipped, distributed training experience, optimization work, hardware awareness. We screen out engineers whose 'deep learning' was Jupyter notebook prototypes that never reached production.
Live optimization exercise
We give the candidate a realistic deep learning optimization problem (e.g., 'this model is too slow / too large / underperforms') with profile data and 90 minutes. They must identify bottlenecks, propose solutions, and discuss trade-offs.
Architecture interview
Whiteboard a deep learning system for a realistic scenario: custom architecture, distributed training, production deployment, hardware constraints. We probe for: architecture decisions, training infrastructure, optimization strategy, deployment patterns.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their deep learning engineer cut our inference cost 5× through quantization, speculative decoding, and serving optimization. Same model quality, dramatically lower infrastructure cost.”
“Best PyTorch engineer I've worked with. He designed a custom Transformer architecture for our specific medical imaging task that outperformed every off-the-shelf model we tried.”
“We needed someone who could ship deep learning to edge devices at production scale. The BearPlex engineer brought hardware-aware design that 'cloud ML engineers' would have missed.”
Hiring deep learning engineers: questions answered
Hire a deep learning engineer when frontier APIs can't solve your problem: you need custom architecture for your specific task, you need self-hosted deep learning for sovereignty / cost reasons, you need edge deployment, or you need optimization beyond what managed services provide. For typical AI features that frontier APIs handle well, AI developers and LLM engineers are usually a better fit.
Yes: common engagement scope. Multi-GPU training (single-node) is standard. Multi-node distributed training (8-64+ GPUs) for larger models. We use DeepSpeed, FSDP, Megatron-LM, and similar frameworks per the workload requirements.
Yes: common for manufacturing, retail, IoT engagements. NVIDIA Jetson (Nano, Xavier, Orin), Google Coral TPU, Apple Core ML, Android with TFLite. We handle model optimization (quantization, pruning, ONNX/TensorRT conversion) plus the engineering work of running deep learning reliably on resource-constrained hardware.
Yes: a core capability. Our deep learning engineers read papers continuously and implement the most-promising ones for client production work. We've implemented techniques from recent computer vision, NLP, multimodal, and reasoning papers for client engagements.
Embedded deep learning engineer: $25K-$45K monthly retainer (typically 6-18 months). Per-project engagement (custom architecture, optimization, edge deployment): $80K-$300K depending on complexity. Deep learning engineering is more expensive than typical engineering due to the senior profile and specialized skill.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours.
Yes: common engagement type, often paired with LLM engineers and fine-tuning engineers. Deep learning engineers focus on the architectural and optimization side (custom heads, hardware-aware training, production serving) while fine-tuning engineers focus on the dataset and training methodology.
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