Hire senior andproven PyTorchengineers
Where most current model work happens, and where GPU cost is decided by whoever writes the training loop. We match you with engineers who write efficient ones. Scored and referenced.
Pick the PyTorch problem. See who we would send.
Vikram IyerSenior ML Engineer95/ 100
Omar HaddadML Engineer91/ 100
Ishaan VermaResearch Engineer88/ 10025,000 applied. 1,000 got in.
Five scored stages, all finished before a profile reaches you. Nobody skips a stage to hit a deadline.
From £15 an hour. That is the whole pricing page.
One hourly rate with our margin printed on every invoice, billed monthly. No placement fee and no notice period beyond two weeks.
£8,314/ monthEquivalent to £48 an hourWhat a PyTorch engineer does
PyTorch engineers design, train, optimize, and ship machine learning models using the PyTorch 2.x stack. They work across model code, data pipelines, GPU performance, evaluation, inference, and deployment. In a modern stack, PyTorch often sits behind Python services, feature stores, experiment tracking, object storage, orchestration tools, and GPU infrastructure. Production teams commonly pair it with TorchVision, TorchAudio, Hugging Face Transformers, Lightning or Accelerate, Triton kernels, ONNX, TensorRT, vLLM, Ray, Kubernetes, and MLflow or Weights & Biases. A mid-level PyTorch engineer can implement known architectures, debug training runs, and use standard libraries well. A senior engineer can reason about numerical stability, distributed training, memory pressure, checkpointing, reproducibility, model export, latency, and failure modes. They know when to use torch.compile, FSDP, AMP, custom CUDA/Triton, quantization, or simpler eager-mode code. They also communicate tradeoffs clearly to product, data, and platform teams.
Typical responsibilities
- Build and maintain PyTorch 2.x training code for deep learning models, including clean nn.Module design, loss functions, metrics, checkpointing, and reproducible experiment configuration.
- Optimize GPU training performance using mixed precision, profiling, data-loader tuning, activation checkpointing, FSDP or DDP, and appropriate batch sizing.
- Prepare models for production inference through torch.compile where appropriate, TorchScript or torch.export paths, ONNX export, TensorRT, vLLM, or service-native deployment.
- Design evaluation pipelines that test model quality, calibration, drift, regression risk, latency, memory use, and behavior on edge cases before release.
- Collaborate with data engineering and platform teams on data loading, distributed storage, orchestration, CI/CD, container images, GPU scheduling, and observability.
- Review PyTorch code for correctness, maintainability, determinism, numerical stability, and safe use of third-party model libraries.
Must-have skills
- Strong Python engineering skills, including packaging, type hints, testing, profiling, async or multiprocessing basics, and maintainable project structure.
- Hands-on PyTorch 2.x experience with tensors, autograd, nn.Module, optimizers, schedulers, DataLoader, custom datasets, checkpointing, and device management.
- Ability to debug training problems such as exploding or vanishing gradients, NaNs, underfitting, overfitting, label leakage, and unstable mixed-precision runs.
- Experience with GPU performance work using CUDA-aware profiling tools, torch.profiler, memory summaries, AMP, pinned memory, and efficient input pipelines.
- Practical distributed training knowledge with DDP, FSDP, gradient accumulation, checkpoint sharding, rank-safe logging, and failure recovery.
- Production ML deployment experience, including model serialization, versioning, batch versus online inference, latency testing, monitoring, and rollback planning.
- Comfort working with modern ML tooling such as Hugging Face Transformers, Lightning or Accelerate, MLflow or Weights & Biases, Docker, Linux, and cloud GPU environments.
Nice to have
- Experience with torch.compile, TorchDynamo, AOTAutograd, Inductor, torch.export, and the practical graph-break issues that affect compiled PyTorch.
- Knowledge of model compression techniques such as quantization, pruning, distillation, LoRA or QLoRA fine-tuning, and memory-efficient inference.
- Ability to write or review custom CUDA, Triton, or C++ extensions when standard PyTorch operators are not fast enough.
- Experience serving LLMs or diffusion models with vLLM, TensorRT-LLM, Hugging Face TGI, Ray Serve, KServe, or similar infrastructure.
- Familiarity with edge or mobile deployment using ExecuTorch, ONNX Runtime, Core ML, or hardware-specific inference runtimes.
8 PyTorch interview questions that reveal real depth
These are the kinds of questions our engineers use in technical interviews. Each comes with what a strong answer sounds like.
Full set: 25 PyTorch questions by level, with red flags- Q1
Walk me through how you would debug a PyTorch training run where loss becomes NaN after several thousand steps.
Strong answer: A strong candidate starts with reproducibility: fixed seed, known batch, saved config, and deterministic settings where practical. They check data for bad labels or infinities, inspect loss scale under AMP, monitor gradient norms, try anomaly detection briefly, reduce learning rate, test without mixed precision, and isolate the layer or operation producing invalid values without making random code changes.
- Q2
When would you use DDP versus FSDP for distributed PyTorch training?
Strong answer: A strong answer distinguishes replication from sharding. DDP is simpler and often best when the model fits per GPU and communication overhead is acceptable. FSDP helps when model, gradients, optimizer state, or activations exceed device memory. They mention sharding strategy, mixed precision, checkpoint format, auto-wrapping, slower debugging, rank-safe logging, and the need to profile rather than assume.
- Q3
What problems can torch.compile solve, and where can it make things worse?
Strong answer: A strong candidate explains that torch.compile in PyTorch 2.x can reduce Python overhead and fuse operations through TorchDynamo, AOTAutograd, and Inductor. They also know it is not magic: dynamic shapes, Python side effects, unsupported ops, graph breaks, custom kernels, and long compile times can hurt. They would benchmark representative workloads and keep a safe eager-mode fallback.
- Q4
How do you design a PyTorch input pipeline that keeps GPUs busy?
Strong answer: A strong answer covers measuring GPU utilization and data-loader wait time first. They discuss efficient dataset formats, avoiding expensive per-sample Python work, batching transforms, num_workers tuning, prefetching, persistent workers, pinned memory, non-blocking transfers, caching where safe, and sharding correctly across ranks. They also mention validating that pipeline changes do not alter labels or evaluation semantics.
- Q5
A model trains well offline but performs poorly after deployment. What PyTorch-specific and system-level issues would you investigate?
Strong answer: A strong candidate checks preprocessing parity, model.eval(), disabled gradients, dropout and batch norm behavior, dtype differences, device placement, tokenizer or feature version mismatch, and export changes. They also examine batch size, padding, truncation, quantization effects, latency timeouts, data drift, and whether the deployed checkpoint exactly matches the evaluated artifact.
- Q6
How would you make PyTorch experiments reproducible without pretending they are perfectly deterministic?
Strong answer: A strong answer records code version, data version, environment, PyTorch and CUDA versions, seeds, configuration, checkpoint, and hardware details. They set deterministic flags where acceptable, control data shuffling, and log all metrics and artifacts. They also note that some GPU kernels and distributed reductions remain nondeterministic or slower when forced deterministic, so reproducibility should be bounded and documented.
- Q7
How do you approach exporting or serving a PyTorch model for production inference?
Strong answer: A strong candidate begins with requirements: latency, throughput, hardware, batch behavior, dynamic shapes, and acceptable numerical drift. They compare eager serving, torch.compile, torch.export, ONNX, TensorRT, or specialized servers. They validate outputs against the training model, test warm-up and concurrency, monitor memory, package preprocessing with the model contract, and keep rollback-friendly model versioning.
- Q8
What code review issues do you look for in PyTorch model code?
Strong answer: A strong answer includes shape assumptions, silent broadcasting, device or dtype bugs, in-place operations that break autograd, missing model.train() or eval() switches, improper no_grad usage, memory leaks from retained graphs, unsafe checkpoint loading, non-rank-aware logging, untested custom losses, and unclear config handling. Senior candidates also look for unnecessary complexity and missing regression tests.
Senior PyTorch engineer job description
A ready-to-paste template. Or skip the posting entirely: send it to us as your brief and get a scored shortlist in 48 hours.
About the role We are hiring a Senior PyTorch Engineer to build, train, optimize, and deploy deep learning models in a production environment. You will work with researchers, data engineers, and platform engineers to turn model ideas into reliable systems. What you'll do Design and maintain PyTorch 2.x model code, datasets, losses, metrics, and checkpoints. Improve training speed and stability on GPU infrastructure. Debug issues with gradients, memory, mixed precision, distributed training, and data quality. Prepare models for inference using the right path: eager PyTorch, torch.compile, torch.export, ONNX, TensorRT, or a model-serving framework. Build evaluation and regression tests that catch model and system failures before release. Document tradeoffs and help other engineers write better ML code. What you'll bring Strong Python and PyTorch experience in real projects. Practical knowledge of CUDA GPUs, AMP, DDP or FSDP, profiling, and memory optimization. Experience shipping ML models, not only training notebooks. Good judgment about when to use frameworks and when to keep code simple. Clear communication with product, data, and infrastructure teams.
What you get with PyTorch
Pre-Vetted Expertise
Every PyTorch developer on OctogleHire passes a rigorous vetting process covering technical skills, communication, and problem-solving ability.
Start in Days, Not Months
Skip the lengthy recruitment cycle. Our PyTorch engineers are ready to integrate with your team and start delivering within days.
Up to 60% Cost Savings
Access world-class PyTorch talent at a fraction of the cost of local hires, without compromising on quality or experience.
Risk-Free Trial Period
Every PyTorch engagement starts with a trial period. If you're not completely satisfied, you pay nothing.
The engagement, as a spec sheet
How OctogleHire compares
Vetting
Resume screening only
Self-reported skills
5-stage technical vetting (1 in 25 pass rate)
Time to Hire
6–12 weeks
Weeks of filtering
48 hours to first match
Cost
$20K–$40K placement fees
Low rates, variable quality
40–60% less, no placement fee
Compliance
You manage it
You manage it
Fully managed (30+ countries)
Replacement
30–90 days (if any)
None
14–30 day replacement guarantee
Support
Account rep (9-5)
Ticket system
Dedicated account manager
“We in need of a handful of developers for our internal CRM build after speaking with multiple agencies and receiving some quotations we sat down with Yaseen from Octogle and we were pleasantly surprised to see what was available for us within their talent pool. we took on three developers saving upwards of circa £6000 per month and developing the software in record time exactly how we wanted it. Highly suggest anyone to use octogle hire if you’re looking for a cost-effective high-quality software development solution.”
Questions engineering leaders ask
A PyTorch developer builds machine learning systems around the PyTorch framework. That can include model architecture, data loading, training loops, evaluation, GPU optimization, distributed training, and deployment. In a production team, they also write tests, track experiments, package models, validate inference outputs, and work with platform engineers on cloud GPU infrastructure, containers, observability, and release workflows.
Hire a PyTorch engineer when the role requires hands-on deep learning implementation, GPU debugging, training performance work, or model deployment from PyTorch artifacts. A general ML engineer may be enough for feature engineering, classical ML, pipelines, or using hosted APIs. For custom neural networks, LLM fine-tuning, computer vision, speech, or high-throughput inference, PyTorch depth matters.
PyTorch is the default choice for many research-to-production deep learning teams because its eager execution model is straightforward and its ecosystem is broad. TensorFlow still appears in mobile, legacy, and some production stacks. JAX is strong for compiler-oriented numerical work and some research teams. Hiring should follow your codebase, deployment target, and team skills rather than framework fashion.
Test practical depth, not API memorization. Ask candidates to debug NaNs, explain autograd behavior, profile a slow training loop, reason about DDP versus FSDP, review model code, and discuss inference export tradeoffs. Good exercises include a small broken training script, a performance bottleneck, or a design review for serving a model under latency and memory constraints.
Productivity depends on your codebase maturity, data access, documentation, experiment history, and GPU environment. A strong senior engineer can usually start reviewing code, reproducing runs, and identifying risks quickly if onboarding is organized. Deeper contributions require access to representative data, model artifacts, deployment context, and clear success metrics. Poor experiment tracking slows even experienced engineers.
Use a freelance PyTorch developer for scoped work such as model review, training optimization, proof of concept, migration, or inference packaging. Hire full-time when the work is core to your product, the model will evolve continuously, or the engineer must own long-term reliability. The key is whether you need a deliverable or sustained technical ownership.
Yes, if the team has strong engineering hygiene. Remote PyTorch work needs reproducible environments, accessible experiment logs, versioned datasets, clear runbooks, secure access to GPU resources, and written design decisions. Code reviews should include model correctness, performance, and deployment impact. Video calls help for debugging, but most collaboration should be captured in issues, PRs, and experiment tracking.
Common mistakes include hiring only from notebook demos, ignoring production experience, failing to test debugging skills, and treating GPU performance as an afterthought. Some candidates can fine-tune a model but cannot explain memory use, distributed training, export failures, or evaluation leakage. Look for engineers who can make models reliable, measurable, and maintainable, not just produce a promising training curve.
You'll receive 3–5 vetted candidate profiles within 48 hours of your demo call. Most companies go from first intro to signed contract in under 5 business days — compared to 6–12 weeks with traditional agencies.
Every developer passes a 5-stage process: application screening, stack-specific technical assessment, live system design interview, soft-skills evaluation, and reference checks. From over 25,000 applicants reviewed, only 1,000 have been accepted into our network.
OctogleHire engineers start at £15/hr (about $19), with senior engineers at about £24/hr. That is roughly 50–65% less than the loaded cost of an equivalent in-house hire in the UK, US or Australia — a senior UK hire costs about £48/hr once employer costs are included. There are no upfront fees or placement commissions; you pay one published hourly rate once your engineer starts.
Every placement includes a risk-free replacement guarantee — 14 days on our On-Demand plan and 30 days on our Marketplace plan. If a developer doesn't meet your expectations, we'll find a replacement at no additional cost and manage the transition for you. Our replacement rate is under 6%.
Send the brief. Meet PyTorch engineers on Thursday.
Twenty minutes with an Octogle engineer, then three to five scored profiles inside 48 hours. Free to post, with a replacement guarantee on every placement.