Hire Machine Learning Engineers | Nearshore Software Development

Machine Learning is transforming industries by enabling applications to learn from data and make intelligent decisions. You need an engineer who can not only build accurate models but also deploy and maintain them in a production environment. Our vetting process, powered by Axiom Cortex™, finds engineers who are masters of the end-to-end machine learning lifecycle. We test their ability to work with data, train models, and use MLOps best practices to deploy and monitor them at scale.

Are your machine learning models stuck in a Jupyter notebook?

The Problem

Building a model is only the first step. Many data scientists struggle to bridge the gap between a research environment and a production system, leaving valuable models on the shelf.

The TeamStation AI Solution

We vet for engineers who are experts in MLOps. They must demonstrate the ability to package, deploy, and monitor machine learning models in a production environment, ensuring they deliver real business value.

Proof: End-to-End Machine Learning Lifecycle
Is your model's performance degrading over time?

The Problem

The world is constantly changing, and a model that was accurate yesterday may not be accurate today. Without proper monitoring, your model's performance can degrade silently, leading to poor business outcomes.

The TeamStation AI Solution

Our engineers are proficient in model monitoring and retraining. They are vetted on their ability to implement systems to monitor for model drift and to automate the process of retraining and redeploying models to ensure they remain accurate and effective over time.

Proof: Continuous Model Monitoring and Improvement

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the Machine Learning domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the Machine Learning space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the Machine Learning discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the Machine Learning domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for Machine Learning across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the Machine Learning domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for Machine Learning

Machine learning fundamentals (supervised, unsupervised learning)
Model development with Python (Scikit-learn, TensorFlow, PyTorch)
MLOps (model deployment, monitoring, CI/CD for ML)
Feature engineering and data preprocessing
Cloud ML platforms (SageMaker, Vertex AI)

Our Technical Analysis for Machine Learning

The Machine Learning Engineer evaluation focuses on the practical application of ML. Candidates are required to build and deploy a machine learning model, demonstrating their understanding of the entire lifecycle. A critical assessment is their ability to use MLOps tools and practices to create a reproducible and automated model deployment pipeline. We also test their knowledge of feature engineering and their ability to work with large, complex datasets. Finally, we assess their experience with cloud-based ML platforms and their ability to choose the right tool for the job.

Related Specializations

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About TeamStation AI

Learn about our mission to redefine nearshore software development.

Nearshore vs. Offshore

Read our CTO's guide to making the right global talent decision.

Ready to Hire a Machine Learning Expert?

Stop searching, start building. We provide top-tier, vetted nearshore Machine Learning talent ready to integrate and deliver from day one.

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