More time for new ideas.
Environment setup, broken jobs, and manual retries eat into research time. We make experiments easier to launch and recover, so your team can test more hypotheses and follow promising ideas sooner.
ML infrastructure consulting for research teams
More experiments.
Less friction.
Your next idea shouldn’t have to wait on infrastructure. We help research teams run more experiments, keep results organized, and get more from their GPUs. Give your researchers more time to explore.
From a focused infrastructure fix to ongoing ownership.
More time for “what if?”
Every experiment, connected.
Which run worked best, and how do you reproduce it? Keep results, configurations, datasets, and checkpoints linked and organized, so everyone can compare runs and build on what the team has already learned.
Shared GPUs. Less waiting.
Stop coordinating GPU access in chat. Automatically schedule jobs across research teams with clear priorities and fair access. Keep more of your fleet doing useful work and more experiments moving forward.
01 / Built around your researchers
Keep the team
focused on discovery.
Every hour lost to infrastructure is an hour your team could spend testing a new idea.
When a researcher spends the morning fixing an environment, chasing a missing checkpoint, or waiting for someone to release GPUs, the next experiment slips. As more teams share the same infrastructure, those small interruptions add up.
AI Infra Dev is an independent ML infrastructure consulting practice. We work alongside your researchers to remove those bottlenecks, from reliable experiment workflows and shared result tracking to automatic job orchestration. We handle the platform work so your team has more room to explore, learn, and iterate.
For research leaders scaling from one team to several, with a platform team that needs support or one still being built.
02 / How we help
From idea to result.
Fewer bottlenecks.
Start where your researchers lose the most time.
Make the next experiment easier to run.
01ML Infrastructure AuditStart hereFind what’s slowing your research down.
Follow an experiment from idea to result. We identify where setup, queues, failed jobs, and scattered tools cost your team time, then review GPU usage and spend. You get a prioritized plan for faster experiments, with clear next steps and a baseline to measure progress.
Research workflows · Experiment turnaround · GPU usage · Priorities02Research Platforms & Job OrchestrationMultiple research teams. Less coordination overhead.
Give researchers a consistent way to launch work without rebuilding environments or asking who has a free GPU. We automate job orchestration across research teams, with shared queues, team quotas, priorities, and recovery from failures. Researchers submit experiments and follow their progress while the platform handles scheduling.
Self-service launches · Automatic scheduling · Team quotas · Recovery03Experiment Tracking & ReproducibilityKnow what worked. Build on it together.
Stop piecing together results from notebooks, chat threads, and folders. We connect each run to its code, configuration, dataset version, metrics, and checkpoints in one shared system. Your team can compare experiments, reproduce promising results, and pick up where a colleague left off.
Run history · Result comparison · Dataset versions · Checkpoint management04GPU Fleet OptimizationMore experiments from the GPUs you already pay for.
Jobs waiting while GPUs sit idle? We find stranded capacity, improve how jobs fit across the fleet, and remove data-loading and networking bottlenecks. The goal is to maximize useful GPU utilization, shorten queues, and give you evidence for when more hardware is actually needed.
Fleet utilization · Workload placement · Data throughput · Capacity planning05Distributed Training & InferenceSpend less time restarting. Get results sooner.
Long runs shouldn’t need constant babysitting. We make multi-GPU and multi-node training more reliable with checkpoint recovery, faster data loading, and performance tuning. We also build efficient batch and online inference so evaluation and deployment can keep pace with your research.
Training throughput · Failure recovery · Serving latency and cost06LLM Fine-Tuning & ServingTry the next model improvement with less setup.
Make it easier to test a new dataset, fine-tuning approach, or model version. We build repeatable pipelines for data preparation, fine-tuning, evaluation, and serving, with checkpoints and results kept together. Your team can compare changes and carry promising models through to deployment.
Fine-tuning · Evaluation · Model optimization · Production servingBuilt around your workflows, with tools your team can maintain.PyTorch · Ray · Kubernetes · AWS · SGLang · vLLM · CUDA
Ongoing infrastructure ownership
Keep research moving.
As your team grows.
Get an experienced ML infrastructure partner one or two days a week. We support researchers, improve the platform, and plan for what’s next as your workloads and teams grow.
Talk about ongoing support- DirectionA platform roadmap tied to your research priorities and the experiments you want to run next.
- OwnershipDay-to-day researcher support, reliable workflows, and visibility into GPU use and spend.
- ContinuityDocumentation, knowledge transfer, and help hiring your future team.
03 / What progress looks like
Less firefighting.
More ideas tested.
Illustrative engagement
Making room for the next experiment.
Several research teams. One shared GPU fleet. Promising ideas waiting behind infrastructure work.
An example of how we work, rather than a reported customer result.
THE CHALLENGE
Researchers negotiate GPU access in chat. Training jobs queue while capacity sits idle, and results live across notebooks and folders. The research lead spends more time unblocking runs than shaping the next experiment.
THE WORK
Set up shared experiment tracking, consistent environments, and automatic job scheduling with team quotas and priorities. Add checkpoint recovery and tune workloads to make better use of the fleet.
SUCCESS CRITERIA
More completed experiments each week, less time lost to setup and queues, reproducible results, and higher useful GPU utilization. Agree on a baseline and measure progress together.
04 / A simple way forward
Start small. Build trust.
Understand
Walk through your research workflow, find where experiments stall, and agree on what to improve first.
Fixed-scope audit$5k–15k
Make it work
Fix a concrete bottleneck, put the changes in researchers’ hands, and measure the difference in their daily work.
Implementation sprint$15k–50k
Keep it working
Support the team and keep improving workflows, reliability, and GPU utilization as research needs evolve.
Fractional partnership$6k–15k / month
Typical ranges. We agree on scope and pricing together before work begins.
05 / Say hello
What’s holding
your research back?
Long queues? Scattered results? Too much time spent keeping jobs running? Tell us where experiments get stuck and what your team wants to try next.
No pitch deck needed. Just a real problem to solve.Make room for your next experiment.
We’ll talk through your research workflow, the friction your team faces, and where infrastructure support could make the biggest difference.
Book an intro callFor the curious
Still building in the open.
Courses, tools, news, and opportunities for AI infrastructure engineers.