Hire in days, not months

Hire Machine Learning Engineers

Ship production-grade machine learning models with senior engineers who focus on accuracy and scale.

From complex model architecture to automated MLOps pipelines, our machine learning engineers help you extract value from data, maintain high model reliability, and keep AI delivery predictable.

Get Matched with ML Talent

ML delivery governance

Governance built for accurate and reliable ML releases

Reduce analytical risk with explicit evaluation controls, bias checks, and performance monitoring tailored to ML projects.

Controls teams ask for before ML launch

Accuracy, security, and bias discipline mapped to how modern ML projects actually ship.

Shortlist turnaround

4.1 days median across recent ML roles

Kickoff speed

10 days median from selection to sprint start

Model reliability

95% of ML models meet quality benchmarks after 90 days

Model evaluation and bias guardrails

Automated testing for model accuracy, fairness, and bias to ensure ML insights are reliable and ethical.

Quality control

Data privacy and security compliance

Secure handling of PII, encrypted data processing, and compliance with data residency requirements for ML workloads.

Security-ready

Model and health monitoring

Real-time tracking of model performance, data drift, and user-perceived value in production.

Health-focused

Talent pool preview

Vetted Machine Learning Engineer profiles ready to interview

Review a balanced shortlist with specialist, senior, and principal depth so you can hire for immediate delivery and long-term technical leadership.

View more profiles

Abdullah W.

Senior ML Engineer

Vetted

8 years

Role-matched

PythonPyTorchMLOpsAWS

Led the ML initiatives for a high-traffic SaaS platform, implementing predictive churn models and MLOps pipelines that reduced user attrition by 20%.

Farhan L.

ML Engineer

Vetted

6 years

Role-matched

PythonTensorFlowRecommendation SystemsGCP

Developed a personalized recommendation engine for a large ecommerce site, increasing average order value by 15% through better product discovery.

Arsalan A.

Principal ML Engineer

VettedArchitect

12 years

Role-matched

System ArchitectureDeep LearningSecurityCI/CD

Architected a secure and scalable ML platform for an enterprise client, improving fraud detection accuracy by 35% while ensuring strict data privacy.

Need a wider shortlist?

We can share additional machine learning engineer profiles by seniority, timezone, and domain fit.

ML engagement options

Choose the engagement model that matches your ML roadmap

Start with focused analytical work or scale to a full ML pod as your product complexity grows.

Model selection support

We map ML role shape to roadmap pressure, technical complexity, and stakeholder expectations.

Part-time ML support

Best for iterative analytical work, model optimization, and ongoing maintenance.

Starts from $2,000 / month

Best for: Steady analytical improvements and maintenance

20-25 hrs/week
ML sprint support
Weekly accuracy reporting

Large-scale data engineering and model training are scoped separately.

Get Started

Full-time ML engineer

Recommended

Best for core ML feature delivery with daily ownership and production momentum.

Starts from $4,000 / month ($25/hour)

Best for: Active ML roadmap execution and product integration

40 hrs/week
Full ownership
Daily accuracy + progress updates

Cloud ML platform costs and third-party licensing are billed separately.

Get Started

ML engineering pod (2 ML Devs + 1 Data Eng + 1 PM)

Best for new product launches, major analytical sets, and cross-functional execution.

Starts from $15,000 / month

Best for: High-stakes initiatives with significant coordination needs

Cross-functional pod
Parallel workstreams
End-to-end orchestration

Specialized security audits are scoped separately.

Get Started

ML hiring process

From ML roadmap to production contribution in under two weeks

The process is tuned for ML delivery risk: model fit, analytical depth, and release reliability.

Typical kickoff window

Most teams start ML delivery with selected talent in 7-14 days.

Decision points are explicit: ML implementation depth, analytical discipline, and communication quality are validated before kickoff.

1
ML goal alignment and scope
Step 1
We map your ML objectives, technical requirements, and business goals to define role scope and success metrics.
Day 1-2
2
Shortlist with relevant ML context
Step 2
Review candidates with prior experience in similar ML domains, analytical patterns, or scale constraints.
Day 2-5
3
Technical validation with ML scenarios
Step 3
Interviews test ML implementation logic, analytical depth, and delivery-specific tradeoff handling.
Day 5-10
4
Onboarding and ML sprint integration
Step 4
Selected engineers join your workflows with clear ownership and immediate first-sprint goals.
Day 7-14

Why product teams hire us for ML

ML execution tuned for accuracy, speed, and operational reliability

You get engineers who can build production-grade ML systems without the overhead of a traditional research team.

Built for high-stakes ML delivery

Designed for teams shipping SaaS products, ecommerce tools, and performance-critical AI experiences.

Typical start

10 days median to sprint kickoff

Accuracy lift

28% median improvement in model accuracy

Deployment speed

35% increase in model release frequency

Fast ramp on ML projects

Engineers integrate into your architecture, analytical patterns, and release flow quickly.

Velocity

Focus on model accuracy and reliability

Engineers prioritize evaluation and validation to ensure ML insights are safe for production.

Reliability

Actionable ML performance

Delivery decisions account for scale, speed, and user-perceived value.

Performance

Service scope

ML use cases mapped to business outcomes, not just research

Use this service scope to match your AI roadmap to the right implementation pattern, whether you need predictive insights, user personalization, or scalable ML infrastructure.

Model Development and Training

Custom machine learning model development

Our ML engineers build robust, production-ready models for classification, regression, and clustering tailored to your specific business requirements.

Deep learning and neural networks

Hire engineers to design and train complex deep learning models using PyTorch or TensorFlow for advanced NLP, vision, or recommendation tasks.

Feature engineering and data preparation

Design and implement efficient feature engineering pipelines that improve model accuracy and reduce data processing latency.

MLOps and Deployment

MLOps and model deployment automation

Automate the deployment, monitoring, and retraining of ML models using Kubeflow, MLflow, or cloud-native tools like SageMaker.

Scalable ML infrastructure and orchestration

Design and implement scalable infrastructure for model training and inference using Kubernetes and Docker, ensuring efficient resource utilization.

Model monitoring and health checks

Implement comprehensive monitoring to track model drift, accuracy, and latency in production, identifying issues before they affect users.

Advanced AI Applications

NLP and text analytics solutions

Develop advanced NLP systems for sentiment analysis, automated categorization, and intelligent content extraction from unstructured data.

Computer vision and image processing

Hire ML experts to build vision-based applications for object detection, image classification, and automated visual inspection.

Recommendation and personalization engines

Design and implement scalable recommendation systems that improve user engagement and conversion through personalized experiences.

Engineering stack

Production-ready ML stack for accuracy, scale, and operational confidence

Stack choices are optimized for fast model iteration, reliability, and scalable data processing across modern AI products.

PyTorch

TensorFlow

Scikit-learn

XGBoost

Python

SQL

Pandas

NumPy

MLflow

Kubeflow

DVC

Weights & Biases

AWS SageMaker

GCP (Vertex AI)

Azure ML

Docker

Kubernetes

Ray

Hiring readiness

ML hiring playbook: evaluate quickly and onboard with less risk

Use this decision hub to align ML interview depth, set quality boundaries, and connect hiring to measurable outcomes.

Role scope Interview playbook Business impact

Responsibilities / Role Scope

Owns

ML model development and evaluation with high accuracy standards
MLOps pipeline architecture and automation
Model monitoring and performance optimization in production
Feature engineering and data preparation workflows

Collaborates on

Product teams to define ML feasibility and user value
Data engineers for efficient data access and pipeline design
DevOps teams for secure model deployment and orchestration
Data scientists for model research and experimental validation

Interview Questions

Structured by level for consistent and faster interviewer calibration.

junior

Fundamentals and execution reliability

What is the difference between supervised and unsupervised learning?
How do you handle missing values or outliers in a dataset?
What is overfitting and how do you prevent it using regularization?
How do you evaluate a classification model's performance?

mid

Delivery ownership and decision quality

How do you choose the right evaluation metric for a specific business problem?
What techniques do you use for feature selection and engineering at scale?
How do you implement a model deployment pipeline using Docker and CI/CD?
How do you handle imbalanced datasets in machine learning?
When would you use a deep learning model versus a traditional ML model?

senior

Architecture, risk control, and leadership

How do you architect a scalable MLOps platform for a multi-team organization?
How do you manage model drift and ensure long-term model performance in production?
How do you approach a complex ML problem with limited or noisy data?
How do you define and communicate the ROI of ML initiatives to leadership?
How would you design a distributed training strategy for a large-scale deep learning model?

Why Outsource This Role

Faster model deployment

Ship production-grade ML models without the overhead of local hiring or complex research delays.

Median kickoff: 10 days from role approval

Predictable ML costs

Scale your ML engineering bandwidth based on active priorities at a predictable hourly rate.

Starts from $25/hour for ML engineering

Improved model accuracy

Reduce errors and improve prediction relevance with engineers who know ML tradeoffs.

Median accuracy lift: 28% in 12 weeks

Lower analytical risk

Use ML best practices and rigorous validation to reduce biased or inaccurate insights.

Model drift incidents reduced 30% quarter-over-quarter

Scalable ML teams

Start with one engineer and expand to a full ML pod as product complexity grows.

ML pod scale-up window: 2-3 weeks

Benefit	Outcome	Metric
Faster model deployment	Ship production-grade ML models without the overhead of local hiring or complex research delays.	Median kickoff: 10 days from role approval
Predictable ML costs	Scale your ML engineering bandwidth based on active priorities at a predictable hourly rate.	Starts from $25/hour for ML engineering
Improved model accuracy	Reduce errors and improve prediction relevance with engineers who know ML tradeoffs.	Median accuracy lift: 28% in 12 weeks
Lower analytical risk	Use ML best practices and rigorous validation to reduce biased or inaccurate insights.	Model drift incidents reduced 30% quarter-over-quarter
Scalable ML teams	Start with one engineer and expand to a full ML pod as product complexity grows.	ML pod scale-up window: 2-3 weeks

Client stories

Trusted by teams that ship fast

Real feedback from partnerships where we embedded with product teams, accelerated delivery, and stayed accountable to outcomes.

“We needed to launch a new product line on a fixed deadline, and missing it would have impacted revenue. Codexty helped us reorganize delivery, close technical gaps, and execute with steady weekly progress updates. We shipped ahead of schedule and exceeded our initial activation targets in the first month.”

Lisa R.

Operations Director, Logistics Company

“Our biggest concern was scalability during a period of rapid growth, and their team handled it with confidence. They refactored key backend services, introduced safer deployment practices, and helped us scale traffic without downtime during peak usage windows. We saw immediate performance gains and far fewer late-night incidents.”

Sarah K.

Engineering Manager, Enterprise Platform

“We needed to scale delivery capacity quickly but were not ready for several full-time hires. Codexty gave us immediate access to vetted talent that integrated into our workflows with minimal ramp-up time. We expanded engineering output while keeping hiring risk and operational overhead under control.”

Chris B.

VP Engineering, Fintech

FAQ

Answers to practical decision questions before you hire.

How quickly can a machine learning engineer start?

Most ML projects begin onboarding within 7-14 days after role alignment and interview completion.

Do you work with PyTorch and TensorFlow?

Yes. We specialize in modern ML development using PyTorch, TensorFlow, Scikit-learn, and various MLOps tools.

Can you help with MLOps and model deployment?

Yes. We support automating the deployment, monitoring, and retraining of ML models using modern MLOps best practices.

How do you handle model drift?

We implement model monitoring and automated retraining to ensure your insights stay fresh and performance stays predictable.

What is the hourly rate for ML engineers?

Our ML engineering services start at $25/hour, providing high-quality analytical delivery at a competitive rate.