Hire senior machine learning engineers.
Hire machine learning engineers who have trained, served, and maintained models in production. A matched shortlist within 72 hours of the scoping call, a working builder in about two weeks, one all-in rate per builder.
11,000+
Vetted builders in the network, under 2% acceptance
72 hrs
From scoping call to a matched shortlist
~2 weeks
From scoping call to a working builder, sometimes sooner depending on scope
3 to 5 months
Typical traditional hiring timeline for a senior engineer. A matched shortlist lands within 72 hours of the scoping call.
Hire a machine learning engineer when the model itself is the work.
Training pipelines that have to finish on schedule. A serving stack that holds its latency target when traffic doubles. Fine-tuning runs on a foundation model, with versioning so you can tell which run produced which behavior. An internal platform that other engineers train and deploy on. That's machine learning engineering. If the work is calling a model API, building retrieval, and writing evals, it's an AI developer scope, and the two scopes pull different people. We'd rather sort that out on the scoping call than in month two.
What most teams try
Post one job description for data, models, and product integration
A listing that asks for pipelines, custom training, and product work describes three senior roles. The candidates who respond can do one of them well and talk fluently about the other two.
Hire an ML engineer for an API integration job
Model infrastructure engineers on integration work either underdeliver, because the work sits below what they're good at, or build serving infrastructure you didn't need. The reverse mis-hire happens too.
Screen on notebooks and benchmark scores
A model that scores well offline and falls over in serving is the usual failure. Production judgment shows up in how a candidate talks about monitoring, retraining triggers, and the last thing that broke.
What A.Team brings
Vetted senior ML talent under your team's management. One rate per builder. A Team Success contact for engagement health.
Senior ML talent from a vetted network
11,000+ vetted builders, under 2% acceptance, six-stage vetting. Average eight to twelve years of professional engineering experience.
A matched shortlist within 72 hours of the scoping call
Matched to your training and serving work, your timezone, and your rate band. You interview. A working builder in about two weeks, sometimes sooner depending on scope.
One rate per builder, stated on the Service Order
One all-in rate per builder on the Service Order, no hidden tiers or success fees. Procurement-ready.
Three steps from scoping call to working builder.
Scoping call
A 30-minute call on the work (training, serving, fine-tuning, or platform), the timezone, the rate band, and the engagement structure. No job description required.
Within 72 hours: Matched shortlist
A matched shortlist of senior builders for your scope. You interview and you select.
About 2 weeks: Working builder
The selected builder starts under your team's management. A Team Success contact runs kickoff and stays reachable throughout.
Ready to scope an ML engagement?
Get a ShortlistHire machine learning engineers for four kinds of work.
Training pipelines
Pipelines for a specific model type that run to completion, restart cleanly, and produce runs you can reproduce. Experiment tracking and model versioning come with them, so the team knows which run produced which model.
Model serving and inference infrastructure
API endpoints, batching, and latency management for models in production, including GPU infrastructure. This is the engineer who builds and operates the layer that the product's model calls depend on.
Fine-tuning pipelines
Fine-tuning workloads on foundation models: the data preparation, the training runs, and the evaluation that tells you whether the tuned model beat the base model at a cost you can live with.
Feature engineering and ML platform work
Features a model can consume, built at scale, and the internal platform that lets other engineers train and deploy without rebuilding the plumbing each time. Who owns features, the ML engineer or a data engineer, depends on your team structure, and the scoping call settles it.
Scoping call to working builder
From the scoping call to a working builder, sometimes sooner depending on scope. Traditional hiring runs three to five months.
Monthly retainer savings vs hourly
Monthly retainers run 5 to 15 percent below the equivalent hourly cost, and they hold the builder's availability for the length of the work.
Conversion fee on a full-time hire
If a contractor converts to full-time, a conversion fee applies. Ask for the formula in writing.
Procurement and engagement governance built in.
One MSA, a Service Order per builder
Standard A.Team commercial structure. Each builder sits on their own Service Order at one all-in rate.
Net-15 payment terms
Standard client-side terms. No hidden tiers or success fees on the Service Order.
Team Success contact
A named contact runs kickoff, checks engagement health, and owns escalation. Your team manages the work day to day.
Replacement if a builder isn't fitting
Replacement with a builder of substantially similar experience and skills, and fast.
Matched to your timezone and rate band
Builders across North America, Europe and Latin America.
Skip the 3-to-5-month search.
A.Team sends a matched shortlist of senior machine learning engineers within 72 hours of the scoping call, at one all-in rate per builder.
Data engineer vs ML engineer vs AI engineer
How the three roles divide the work, where they overlap, and the three most common mis-hires. Read it before you write the scope.
AI engineer rates in 2026
The full rate breakdown for senior AI engineers, ML engineers among them: base rates, the AI premium, and the hidden costs most comparisons miss.
Common questions about hiring machine learning engineers through A.Team
It depends mostly on the kind of work (training, serving, or platform), the seniority, and the region. A.Team publishes the full rate breakdown for senior AI roles, ML engineers included, in the AI engineer rates guide. Every engagement carries one all-in rate per builder on the Service Order, with no hidden tiers or success fees, and monthly retainers run 5 to 15 percent below the equivalent hourly cost.