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Machine Learning Engineers

Hire ML Engineers for Model Training and MLOps

Hire ML engineers to build training pipelines, tune models, and manage production workflows.
  • Expertly vetted in model training, evaluation, and MLOps
  • Experienced with MLflow, Kubeflow, SageMaker, and Python
  • Matched to training pipelines, feature stores, and model workloads
  • Fully dedicated and managed by your data science or ML platform leader
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Remote ML Engineer in Poland ready for hire on 1840 & Company
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Hire Machine Learning Engineers From Global Talent Markets

Hire machine learning engineers from global markets with the best cost-to-skill ratio for model training and MLOps work.

Model Pipeline Screening

Vetted for training pipelines, model evaluation, and feature work.

Lower Model Team Costs

Reduce development costs by up to 83% through global ML engineer hiring.

ML Talent Hired in 2 Weeks

Receive ML engineer profiles in 3–5 days with hires completed within two weeks.

Dedicated Model Workflow

Your ML engineer works full-time inside your model workflow. No shared resources.

MLOps Stack Alignment

MLflow, Kubeflow, SageMaker, Python, and deployment workflows experience.

Training Run Coverage

Engineers matched to training runs, deployment cycles, and collaboration hours.

ROLE OVERVIEW

What ML Engineers Own Across the Model Lifecycle

Machine learning engineers prepare training data, build features, and evaluate model outputs. They also support deployment handoffs and production model workflows.

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Data Preparation & Feature Engineering

Before training begins, ML engineers clean source data, validate labels, build features, and structure training and test sets.

ML Workflow Scope

  • Data cleaning and transformation
  • Feature engineering
  • Dataset structuring for training and testing
  • Label validation and data quality checks
  • Training and test set preparation
  • Pipeline handoff for model training workflows

Modeling and MLOps Tools

Python, pandas, NumPy, SQL, Spark, Airflow, Jupyter, cloud storage systems, and data warehouse environments.

Model Lifecycle Use Cases

  • Preparing structured datasets for model training
  • Creating features for classification, prediction, or ranking models
  • Cleaning and validating training data before experimentation
  • Data handoff between engineering and model training teams
A ML engineer trains models, tunes parameters, evaluates outputs. They are responsible for documenting results through experiment tracking workflows.

ML Workflow Scope

  • Running model training workflows
  • Tuning hyperparameters across model runs
  • Evaluating and comparing model outputs
  • Tracking experiments across model versions
  • Reviewing model errors and output patterns
  • Supporting retraining based on model results

Modeling and MLOps Tools

scikit-learn, TensorFlow, PyTorch, XGBoost, MLflow, Jupyter, Python, and model evaluation libraries.

Model Lifecycle Use Cases

  • Training supervised, classification, regression, or ranking models
  • Comparing model outputs across experiments
  • Tracking model versions, results, and evaluation metrics
  • Supporting retraining cycles after data or performance changes
After training, ML engineers prepare models for deployment. They manage versioning, and support batch or real-time inference workflows.

ML Workflow Scope

  • Model deployment workflows
  • CI/CD support for ML systems
  • Model versioning
  • Monitoring and retraining workflows
  • Batch and real-time inference support
  • Cloud-based ML infrastructure support

Modeling and MLOps Tools

MLflow, Kubeflow, SageMaker, Docker, Kubernetes, AWS, Azure, GCP, CI/CD tools, and model monitoring platforms.

Model Lifecycle Use Cases

  • Moving trained models into production workflows
  • Supporting batch or real-time inference systems
  • Managing model versions across environments
  • Structuring deployment workflows for internal ML platforms
WHEN TO OUTSOURCE

When Model Pipelines Need Dedicated Ownership

Global workforce across 150 countries with finance, tech, sales, and support teams

Machine learning outsourcing fits when model training, pipeline work, or deployment infrastructure is defined but lacks dedicated ownership across the workflow.

Training Data Is Not Ready for Modeling

odel work slows when datasets need cleaning, label checks, or feature preparation before training begins. Hiring machine learning engineers gives training and test data preparation a dedicated owner.

Experiments Lack Consistent Tracking

Model runs are hard to compare without consistent tracking. A dedicated ML engineer owns experiment documentation, evaluation outputs, and model comparison workflows.

Model Evaluation Slows Release Cycles

Models need repeatable evaluation before production handoff. An offshore ML engineer compares outputs, reviews errors, and supports retraining based on model results.

Deployment Handoffs Stay Unclear

Trained models require packaging, versioning, and defined handoff steps before production use. Hire remote ML engineers to support deployment workflows, model versioning, and batch or real-time inference requirements.

WHY GLOBAL TALENT

Why Build ML Teams Through Global Talent Markets

Expand reach beyond local hiring markets to reduce overhead costs and access remote ML engineers with MLOps experience.

Find Pipeline-Ready Talent

Local markets may have limited training pipeline, evaluation, and MLOps experience. Global sourcing expands access to ML engineers with lifecycle skills.

Cover Active Model Work

Model pipelines slow when data preparation, evaluation, or deployment tasks have no dedicated owner.Global hiring adds faster coverage across active pipeline stages.

Align Training Cycles

Remote machine learning engineers are matched to training runs, deployment cycles, and collaboration hours for existing model workflows.

Reduce Model Team Costs

Global hiring adds machine learning capacity without permanent local headcount. Global hiring adds dedicated ML engineer capacity without permanent local headcount costs.
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SALARY BENCHMARKS

Machine Learning Engineer Cost and Salary Comparison

See how much you can save by hiring remote machine learning engineers from cost-effective regions.

$15,000/month

Average US Salary

$2,500/month*

India Average Salary

83%

Potential savings

$4,500/month*

Average LATAM Salary

70%

Potential savings

VS

Offshore Vs Local Customer ML Engineer Salaries

Local US Hire India LATAM
Annual Base Salary $180,000 $30,000 $54,000
Payroll Taxes (7.65%-10%) $18,000 $0 $0
Benefits (20%-30%) $36,000 $0 $0
PTO (5%-10%) $9,000 $0 $0
Total Cost $234,000 $30,000 (excluding service fees) $54,000 (excluding service fees)
Savings - $204,000 $180,000

* Salaries shown are based on publicly available data (Indeed, Glassdoor, etc.), showing average rates by role and location. Offshore rates do not include outsourcing provider fees. Please schedule a consultation to receive detailed information tailored to your needs.

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LOCATIONS

Where to Hire ML Engineers Across Global Markets

Hire ML engineers by model workload, coverage needs, and MLOps scope:
  • Argentina: Nearshore model training, evaluation, and workflow support
  • Philippines: Data preparation, documentation, and evaluation tracking
  • India: Offshore ML engineering for training, evaluation, and MLOps
  • Ukraine: Backend-connected model workflows and production handoffs
  • Poland: Cloud, MLOps, versioning, and European collaboration hours

Argentina

Regional Expertise:

Machine learning engineers in Argentina have expertise in training pipelines, feature engineering, experiment tracking, and model evaluation. Shared working hours support collaboration with U.S.-based data and engineering teams during active model work.

Ideal For:

Businesses requiring nearshore ML engineers for model training, evaluation workflows, feature engineering, and pipeline support that requires U.S. workday overlap.

Estimated Cost:

$2,500 – $4,500 per month

Regional Expertise:

ML engineers in the Philippines have experience in structured data preparation, dataset documentation, evaluation tracking, and repeatable model workflow tasks. This market supports defined ML work across extended coverage hours.

Ideal For:

Companies hiring offshore machine learning engineers for dataset preparation, model documentation, evaluation tracking, and structured pipeline tasks across extended working hours.

Estimated Cost:

$1,800 – $3,500 per month

Regional Expertise:

ML engineers based in India offer expertise across Python, data pipelines, model training, feature engineering, model evaluation, experiment tracking, and MLOps tools. This region supports defined ML workloads and extended development coverage.

Ideal For:

Businesses that require offshore ML engineers for training pipelines, model evaluation, MLOps workflows, deployment support, and defined technical requirements.

Estimated Cost:

$1,500 – $2,500 per month

Regional Expertise:

Ukrainian ML engineers have experience in software engineering, backend systems, data pipeline handoffs, model workflows, and deployment infrastructure. They can support ML work connected to existing engineering systems and production handoffs.

Ideal For:

Companies looking for remote ML engineers for backend-connected model workflows, training pipelines, evaluation support, deployment infrastructure, and production handoffs.

Estimated Cost:

$2,000 – $4,000 per month

Regional Expertise:

This region has expertise across cloud systems, data infrastructure, MLOps environments, model versioning, monitoring, and batch or real-time inference systems. ML engineers in Poland support European collaboration hours and production ML environments.

Ideal For:

Businesses hiring remote engineers for MLOps, cloud-based deployment workflows, model versioning, monitoring, inference systems, and European working-hour coverage.

Estimated Cost:

$3,000 – $5,500 per month

Regional Expertise:

U.S.-based ML engineers have direct overlap with model review cycles, planning sessions, and stakeholder meetings. They support evaluation planning, experiment review, and production workflow coordination.

Ideal For:

Companies that need engineers for U.S. working-hour alignment, model reviews, technical planning, and internal stakeholder collaboration.

Estimated Cost:

$9,000 – $15,000 per month

MODEL COMPARISON

How 1840 Compares to ML Engineer Hiring Models

See how we compare to traditional machine learning engineer staffing agencies and freelance options.

Freelance Marketplaces

Traditional Staffing Agencies

Engagement Structure

Full-time, dedicated role

Hourly or project-based

Permanent hire or temp placement

Role Ownership

Client-managed, embedded in your team

Independent contractor

Client-managed after placement

Vetting & Screening

Multi-stage vetting + English validation

Self-reported profiles

Resume screening + interviews

Industry & CRM Alignment

Matched by role, industry, and tools

Varies by individual

General experience matching

Commitment & Stability

Long-term role continuity

Often juggling multiple clients

Depends on employee retention

Upfront Fees

None

None

15–30% placement fee typical

Replacement Support

No-cost replacement guarantee

You must rehire yourself

Often additional fees apply

Cost Efficiency

Up to 83% lower than U.S. hiring

Variable hourly rates

High salary + agency fees

Global Talent Access

Nearshore & offshore sourcing

Global, unstructured

Primarily local markets

Scalability

Build one role or entire team

Difficult to standardize

Slower hiring cycles

How It Works

3 Steps to Hire Machine Learning Engineers

Request A Quote

Define Your Model Workload

We begin with a discovery call to define the model workload, tools, and team structure. We use those details to source ML engineers for data preparation, feature engineering, training, evaluation, deployment, or MLOps support.

Interview Matched ML Talent

Within 3 to 5 days, we deliver full-time ML engineer profiles matched to your workload, tools, coverage needs, and team structure. You interview the candidates and select the candidate aligned with your model workflow.

Hire and Onboard Your ML Engineer

We manage onboarding, contracts, payroll, and compliance after selection. Your ML engineer starts within two weeks and supports training data, model evaluation, experiment tracking, deployment, or MLOps tasks.

Hire Machine Learning Engineers With Zero Risk

No Upfront Fees

We source, vet, and present candidates before you pay.

Interview First

Meet and approve candidates before they join your team.

Free Replacements

If a hire doesn’t work out, we replace them at no cost.

Payroll & Compliance

We manage international payroll and employment compliance.

Top-rated by Clutch

We’re proud to be named by Clutch as a trusted leader in outsourcing.

Clutch Top BPO Award
Clutch Top Inbound Appointment Setting Company Award
Clutch Award for Top Call Centers

FAQs for Hiring Machine Learning Engineers

Choose an ML engineer for training data, feature engineering, model training, evaluation, experiment tracking, deployment, or MLOps. An AI developer is the better match for LLM APIs, prompt logic, RAG, AI product features, and workflow automation inside existing systems.

Yes. Machine learning engineers are matched to your tools and workflows during sourcing. Common tools include Python, SQL, pandas, NumPy, scikit-learn, TensorFlow, PyTorch, XGBoost, MLflow, Kubeflow, SageMaker, Docker, Kubernetes, AWS, Azure, and GCP.

Yes. Hired machine learning engineers are placed in full-time, dedicated roles. They work only with your team on your model workflows, datasets, training runs, evaluation tasks, and deployment requirements.

Yes. Coverage hours are confirmed before sourcing begins. Depending on your needs, we can source ML engineers for U.S. overlap, European collaboration hours, or offshore schedules tied to training runs and deployment cycles.

Remote ML engineers can cost up to 83% less than U.S.-based ML roles. Pricing depends on market, seniority, schedule, and technical scope, including model training, feature engineering, evaluation, MLOps, and deployment support.

We handle the replacement process at no additional cost. The replacement profile is sourced against the same model workload, tools, seniority, schedule, and model workflow requirements.

ML engineer profiles are typically delivered within 3 to 5 days. Most clients complete interviews, selection, onboarding, and role start within two weeks. Timelines may extend depending on seniority level, MLOps tools, cloud requirements, working hours, or model workflow scope.

Request a Quote
TESTIMONIALS

What Our Clients Are Saying

1840 & Company provided us with more transparency into our sales calls at a time when we really needed it. They provided QA tools, improved our value proposition, and sales coaching which greatly improved our customer interactions and sales process.

Casey H

Manger Of Training, Homelight

Although we had several partners with more tenure, it was 1840 & Company that used their analysis team to open up our opportunities with NPS score, resulting in 50%+ improvements.

Danielle R

VP of Client Services, Pentius

The team completes tasks on time and goes above and beyond. I’ve been pleased with everything so far.

Mike M

Owner, Carve Financial

We selected 1840 based on our needs and their ability to source and onboard offshore talent. We were impressed with their clear and concise communication.

Andrew C

VP Finance, Bluestone Lane

They consistently find people that fit the client’s requirements for each position. Moreover, the resources they provide are usually ready to work right away. They’re like a matchmaker for staff; they provide exactly what we’re looking for.

Undisclosed

CEO, Startup Incubator

The team sourced candidates for us from the Philipines to do data verification for our company and now we work with them to do payroll. They moved quickly and provided quality candidates.

Undisclosed

HR Manager, Tech Company

They achieved KPIs, closed B2B sales, and exceeded our sales expectations with monthly growth. The team shows a willingness to adapt and take over additional LOBs and provided efficient management. Great communication and quick, good results.

Undisclosed

Senior Vendor Manager, Ooma

The team sourced candidates for us from the Philipines to do data verification for our company and now we work with them to do payroll. They moved quickly and provided quality candidates.

Undisclosed

HR Manager, Tech Company

1840 & Company provided us with more transparency into our sales calls at a time when we really needed it. They provided QA tools, improved our value proposition, and sales coaching which greatly improved our customer interactions and sales process.

Casey H

Manger Of Training, Homelight

Although we had several partners with more tenure, it was 1840 & Company that used their analysis team to open up our opportunities with NPS score, resulting in 50%+ improvements.

Danielle R

VP of Client Services, Pentius

The team completes tasks on time and goes above and beyond. I’ve been pleased with everything so far.

Mike M

Owner, Carve Financial

We selected 1840 based on our needs and their ability to source and onboard offshore talent. We were impressed with their clear and concise communication.

Andrew C

VP Finance, Bluestone Lane

They consistently find people that fit the client’s requirements for each position. Moreover, the resources they provide are usually ready to work right away. They’re like a matchmaker for staff; they provide exactly what we’re looking for.

Undisclosed

CEO, Startup Incubator

They achieved KPIs, closed B2B sales, and exceeded our sales expectations with monthly growth. The team shows a willingness to adapt and take over additional LOBs and provided efficient management. Great communication and quick, good results.

Undisclosed

Senior Vendor Manager, Ooma

They achieved KPIs, closed B2B sales, and exceeded our sales expectations with monthly growth. The team shows a willingness to adapt and take over additional LOBs and provided efficient management. Great communication and quick, good results.

Undisclosed

Senior Vendor Manager, Ooma

The team sourced candidates for us from the Philipines to do data verification for our company and now we work with them to do payroll. They moved quickly and provided quality candidates.

Undisclosed

HR Manager, Tech Company

They consistently find people that fit the client’s requirements for each position. Moreover, the resources they provide are usually ready to work right away. They’re like a matchmaker for staff; they provide exactly what we’re looking for.

Undisclosed

CEO, Startup Incubator

We selected 1840 based on our needs and their ability to source and onboard offshore talent. We were impressed with their clear and concise communication.

Andrew C

VP Finance, Bluestone Lane

The team completes tasks on time and goes above and beyond. I’ve been pleased with everything so far.

Mike M

Owner, Carve Financial

Although we had several partners with more tenure, it was 1840 & Company that used their analysis team to open up our opportunities with NPS score, resulting in 50%+ improvements.

Danielle R

VP of Client Services, Pentius

1840 & Company provided us with more transparency into our sales calls at a time when we really needed it. They provided QA tools, improved our value proposition, and sales coaching which greatly improved our customer interactions and sales process.

Casey H

Manger Of Training, Homelight

1840 & Company provided us with more transparency into our sales calls at a time when we really needed it. They provided QA tools, improved our value proposition, and sales coaching which greatly improved our customer interactions and sales process.

Casey H

Manger Of Training, Homelight

Although we had several partners with more tenure, it was 1840 & Company that used their analysis team to open up our opportunities with NPS score, resulting in 50%+ improvements.

Danielle R

VP of Client Services, Pentius

The team completes tasks on time and goes above and beyond. I’ve been pleased with everything so far.

Mike M

Owner, Carve Financial

We selected 1840 based on our needs and their ability to source and onboard offshore talent. We were impressed with their clear and concise communication.

Andrew C

VP Finance, Bluestone Lane

They consistently find people that fit the client’s requirements for each position. Moreover, the resources they provide are usually ready to work right away. They’re like a matchmaker for staff; they provide exactly what we’re looking for.

Undisclosed

CEO, Startup Incubator

The team sourced candidates for us from the Philipines to do data verification for our company and now we work with them to do payroll. They moved quickly and provided quality candidates.

Undisclosed

HR Manager, Tech Company