Last updated: July 11, 2026
Deep Learning Engineer Staffing For Teams Training Real Models
We place deep learning engineers who design, train, and ship neural networks in production, from multi-GPU training runs to quantized inference. Hire the engineer your AI roadmap actually needs, not a Kaggle hobbyist.
Last updated: July 11, 2026
KORE1 places production deep learning engineers across the United States, averaging 17 days to hire with a 92% 12-month retention rate, with deep coverage across PyTorch, JAX, CUDA, Hugging Face, and multi-GPU training stacks.
Deep learning talent is where hiring gets expensive. Mis-hires cost the most here. The field is small. The people who have actually trained a model past a few billion parameters, wrangled a stalled all-reduce across 32 GPUs, and shipped it behind a latency budget are smaller still. Most are already employed. Most will not answer a generic recruiter’s cold email.
Deep learning engineering is a specialty within our broader IT staffing services practice and a focused spoke under our AI/ML engineer staffing pillar. We screen against the parts of the job that do not show up on a resume. That means PyTorch and JAX, sure, but also mixed-precision training, gradient checkpointing, data and model parallelism, CUDA kernel profiling, and the quiet skill of reading a diverging loss curve at 2am and knowing whether it is the data, the learning rate, or a bad shard. For post-deployment reliability, see MLOps engineer staffing. For the broader lifecycle role, see machine learning engineer staffing.
Training a Model Is Not the Same as Shipping One
A notebook that hits 94% on a held-out set is the easy 20%. The hard 80% is everything after. Reproducible training runs. Checkpoints you can actually resume. A data pipeline that does not silently poison the batch. Inference that fits your latency and cost budget once the demo becomes a product.
The engineers who can do all of that are rarely the ones with the flashiest research pedigree. They are people who have felt the specific pain of a training job that crashed at hour 40 with no checkpoint, and who architected the next one so it never happened again. We know what that background reads like. Most recruiters see “PyTorch” on a resume and stop reading. The annual State of AI Report keeps naming the same bottleneck year after year, and it is not compute. It is people who can take a model from a paper to a paying customer.
We placed a senior deep learning engineer at a medical imaging startup last quarter. The client’s first job description asked for a PhD and five first-author papers. The person who closed the role had a master’s, four years of production CV work, and a story about cutting inference cost 60% by quantizing a segmentation model without losing a point of Dice score. That story was the screen.
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Roles Within Deep Learning We Staff
“Deep learning engineer” hides at least half a dozen different jobs. Same title. Very different work. A person who fine-tunes vision transformers on edge hardware is not the person who trains a 70B language model across a GPU cluster. Hiring well starts with naming the lane. We screen for the lane you actually need, not the title trending on LinkedIn this month.
- Deep Learning Research Engineer — novel architectures, ablations, paper-to-code
- Applied Deep Learning Engineer — embedded with a product team, ships models to users
- Training Infrastructure Engineer — distributed training, GPU clusters, throughput tuning
- Computer Vision Engineer — detection, segmentation, edge and real-time inference
- NLP Engineer or LLM Engineer — transformers, RAG, fine-tuning, eval harnesses
- Generative AI Engineer — diffusion, multimodal, agentic pipelines
- Inference Optimization Engineer — quantization, distillation, TensorRT, Triton serving
- MLOps Engineer — retrain triggers, drift, on-call for models in production
Calibrating an offer? Our machine learning engineer salary guide covers the closest comp bands, and our ML engineer interview questions help you tighten the technical loop before the first screen.
Hiring Models That Match Your Roadmap
No two AI teams hire on the same clock. Some need a senior engineer on the training pipeline yesterday. Others want a strong contractor through a launch and a permanent hire after. Pick the model that fits the work.
Contract
For a defined training push, a model migration, or interim coverage. Fast onboarding, no long-term commitment.
Contract-to-Hire
Watch the engineer ship on your actual stack before you convert. It kills the worst risk: the senior who can’t train.
Direct Hire
Permanent placement when the role is core to the platform. Deep vetting and culture-fit calls included.
Project-Based
An embedded deep learning squad for a defined initiative. A recsys rebuild, a fine-tuning program, or a fresh model launch.
How We Hire Deep Learning Engineers
Name the Lane
An intake call with your hiring manager and lead engineer. We pin down the lane, the stack, the model scale, and the must-haves. We push back on job description language that filters strong candidates out before screen one.
Vetted Shortlist Inside Two Weeks
Senior recruiters screen against the stack and the training experience that matters. You get candidates with a written brief, not a resume dump. Average shortlist size is three to five.
Offer, Close, Stay
We support comp calibration, counter-offer prep, and the first 90 days. Our 92% 12-month retention is not luck. It is how we close.
Recruiters Who Know a Trained Model From a Fine-Tuned One
- Recruiters who have placed AI talent through every GPU shortage, framework war, and hype cycle since 2005
- Technical screens calibrated to the lane, not a generic “so, do you know PyTorch?”
- Comp benchmarks anchored to your stack, region, and model scale
- Real reference checks with the engineers who actually shipped alongside the candidate
- Honest pushback when a job description is quietly filtering out the people who would deliver
Talk to a Specialist →“Anyone can screen for the word transformer. The engineers we present have been vetted by a recruiter who can tell the difference between someone who read the paper and someone who has debugged a divergent training run at 3am. That is what the 92% retention number is really measuring.”
— Devin Hornick, Partner at KORE1
Common Questions
How long does it take to hire a deep learning engineer through KORE1?
Our average time-to-hire for AI roles is 17 days from intake to signed offer, with a vetted shortlist usually landing inside two weeks. Deep learning searches trend toward the longer end when the role demands large-scale distributed training or frontier-model experience, sometimes 4 to 6 weeks, because the stack-fit screening takes more cycles. We tell you up front whether yours is a fast search or a deep one.
What does a deep learning engineer actually cost in 2026?
Mid-level deep learning engineers in the U.S. land around $175K to $225K base, with senior engineers in the $250K to $360K range, and specialists in LLM or foundation-model training pushing well past that. Factor in another 20 to 40% for total comp once equity and bonus are on the table. For context, the U.S. Bureau of Labor Statistics projects software developer roles, the category that houses deep learning engineers, to grow 17% through 2033, far above the average across occupations. Our machine learning engineer salary guide breaks the closest bands down by region and stack.
What’s the difference between a deep learning engineer and a machine learning engineer?
A deep learning engineer goes deep on neural networks, GPU training, and model architecture. A machine learning engineer owns the broader lifecycle across many model types, including plenty that are not neural at all. One goes deep. The other goes wide. Many teams that ask for a deep learning engineer actually need a full-lifecycle machine learning engineer, and we help calibrate that before the first screen.
Do I need a deep learning engineer or a data scientist?
Short version: a data scientist tells you which model to build, and a deep learning engineer builds and trains it to run in production. A data scientist’s deliverable is often an insight or a prototype. A deep learning engineer’s deliverable is a trained model that serves real traffic under a latency budget. If your neural network already works in a notebook and needs to survive contact with users, you need the engineer.
Can KORE1 find engineers with LLM and generative AI experience?
Yes. It is one of our busiest lanes right now. We place engineers who fine-tune open-weight models, build RAG and eval pipelines, and train diffusion and multimodal systems. For roles centered on large language models specifically, pair this page with our LLM engineer staffing and generative AI engineer staffing services so we can target the exact experience your build needs.
Do you place remote deep learning engineers or only on-site?
Roughly 70% of our 2025 and 2026 AI placements have been remote or hybrid. We staff across 30+ U.S. metros and screen for time-zone overlap with your team when the role calls for it. Fully on-site searches still happen, mostly for defense, regulated healthcare, and finance clients with secure-environment or on-prem GPU requirements.
Hire a Deep Learning Engineer Who Ships to Production
Tell us the lane, the stack, and the model scale. We’ll bring back a vetted shortlist of engineers who can train it, optimize it, and keep it running after launch.