Hire Machine Learning Developers

Hire vetted machine learning developers across computer vision, deep learning, MLOps, and generative AI work, matched to your stack and ready to interview. Choose full-time or part-time engagement; DOIT handles sourcing, contracts, payroll, and onboarding so your team focuses on the build.

Start hiring

Hire machine learning developers in 5 steps

Share your role brief

Get matched profiles

Review ML developers

Interview the best

Onboard with full support

Share your role brief

You share role requirements covering stack, scope, seniority, ML domain, and availability. The DOIT hiring team asks every clarifying question upfront so the search starts with full context.

Get matched profiles

DOIT filters the talent network on hands-on ML engineering signal, stack fit, ML domain background, and collaboration style. First relevant pre-screened profiles typically reach you within 3 to 5 business days.

Review ML developers

Each profile arrives with a pre-interview video recording, a stack snapshot, rate disclosure, and the developer's availability upfront. You see only candidates matching your technical and collaboration requirements.

Interview the best

You interview the best machine learning developers you want to meet. DOIT coordinates scheduling and runs additional technical rounds per need.

Onboard with full support

After you select, DOIT will help you onboard a machine learning developer and cover legal tasks. The team also stays in touch to help with payroll, HR, and administrative management and conduct feedback sessions to ensure ongoing satisfaction with the developer's work.

Why hire machine learning engineers with DOIT

DOIT helps startups and SMBs hire machine learning developers who understand how ML actually works on real products. Every developer your team interviews is already vetted on stack depth and collaboration fit.

Vetted ML expertise

Every machine learning developer passes screening for hands-on ML work across training pipelines, deployment, monitoring, and maintenance. Your team interviews only developers whose technical depth matches the role.

Middle and senior only

Hire machine learning engineers at middle and senior levels who can contribute to production work immediately. No junior placements, so your team starts shipping faster after onboarding.

Full hiring pipeline managed

DOIT handles sourcing, screening, contracts, payroll, and onboarding while your team focuses on interviews and integration. The hiring overhead is fully off your plate.

Flexible engagement

Hire dedicated machine learning developers full-time or part-time at 20 hours per week. Scale the engagement up or down with no long-term commitment.

Talent guarantee

If a placement does not work out, DOIT delivers a free replacement and supports knowledge transfer so the project stays on track. The guarantee covers the full engagement period.

Global reach

Hire remote machine learning developers across 10 countries with time zone overlap aligned to your working hours. DOIT manages onboarding and cross-geography admin so collaboration with your team runs smoothly.

Tell DOIT what ML work your team is hiring for

Share your requirements and get matched ML developers aligned to your stack.

Get Matched

How DOIT vets machine learning developers

Experience review

DOIT reviews each developer's resume and portfolio for hands-on ML work history, then runs a live interview to assess technical depth, soft skills, collaboration style, and English fluency.

Pass rate: 14.3%

Technical vetting

DOIT can run technical scenarios and test tasks per need, aligned to the role's ML requirements such as feature engineering, training pipelines, evaluation, and deployment workflows.

Pass rate: 5%

Fit confirmation

The hiring team pairs you with product-driven ML developers who integrate into your existing engineering setup, share collaboration habits with your team, care about the outcomes your roadmap targets, and stay engaged with the role over time.

Pass rate: 1.5%

Hire the right ML developer for your project

Machine Learning Engineer

Hire ML engineers experienced in building and deploying ML models.

Engagement levels span middle, senior, staff, and principal seniorities, depending on the role.

Applied Machine Learning Engineer

Hire applied ML engineers who embed with product teams to apply ML to specific business problems.

They handle feature engineering, training experiments, model evaluation, and online iteration as part of the team’s day-to-day work.

MLOps Engineer

Hire MLOps engineers who own ML pipelines, model registries, serving infrastructure, and observability work.

Common skills include MLflow, Kubeflow, Weights & Biases, BentoML, and Apache Airflow.

Deep Learning Engineer

Hire deep learning engineers who design and train neural network architectures across vision, language, audio, and multimodal applications.

They work with PyTorch, TensorFlow, JAX, and Hugging Face Transformers on both research-grade and shipping work.

Computer Vision Engineer

Hire computer vision engineers experienced in object detection, image segmentation, video analytics, and vision-language models.

Their work usually involves OpenCV, YOLO11, SAM2, and PyTorch across real-time and batch pipelines.

LLM Engineer

Hire LLM engineers who build language model applications, including fine-tuning, RAG pipelines, agentic workflows, and evaluation harnesses.

Production stacks include Hugging Face Transformers, LangChain, LlamaIndex, vLLM, and Ollama across closed and open-weight models.

Generative AI Engineer

Hire generative AI engineers who integrate text, image, audio, and video generation into product features.

Common skills include FLUX, Stable Diffusion 3, Whisper, and the OpenAI API.

Hire dedicated machine learning engineers with the right tech stack

Start with a free consultation, and DOIT will help you find a relevant technical and cultural fit for your team.

Talk to hiring experts

Hire machine learning developers with advanced tech skills

Machine Learning

Deep Learning and Computer Vision

Generative AI and LLM

MLOps

Python

SQL

PyTorch

TensorFlow

scikit-learn

Polars

Pandas

NumPy

JAX

Hugging Face Transformers

OpenCV

YOLO11

SAM2

LangChain and LlamaIndex

vLLM

Ollama

OpenAI & Claude APIs

Open-weight LLMs

Image & speech

Spark

Kafka

Ray

Dask

Kubeflow

MLflow

Python

The standard language for ML work, with the deepest framework ecosystem of any language available today. Hire Python developers for training models, deploying inference, building features, and integrating ML into product systems.

Universal ML language

Deepest library ecosystem

Easy team onboarding

SQL

The data layer language behind training feature engineering and production data access. Hire SQL-fluent ML developers when the role spends meaningful time in the data warehouse.

Direct data access

Strong feature pipelines

Standard data layer

PyTorch

The dominant deep learning framework for new ML work in 2026. Hire PyTorch developers for model training, fine-tuning open-weight LLMs, inference pipelines, and integration with product code.

Production-ready training

Research-friendly syntax

Strong community support

TensorFlow

A strong deep learning framework with mature serving tooling for cloud and edge. Hire TensorFlow developers for enterprise stacks and new TensorFlow Extended pipelines.

Enterprise-grade serving

Edge-ready deployment

Mature MLOps tooling

scikit-learn

The classical ML standard for tabular data and feature engineering work. Hire scikit-learn developers for classification, clustering, dimensionality reduction, and forecasting projects.

Fast prototyping

Classical ML standard

Clean Python API

Polars

A modern Python-native DataFrame library gaining adoption for large-scale feature engineering. Hire Polars-experienced developers for feature pipelines where Pandas hits scaling limits.

Fast

Memory-efficient

Large-data ready

Pandas

A data manipulation and analysis library for handling structured data in ML workflows. It provides DataFrames for loading, cleaning, transforming, and analyzing tabular datasets before model training.

Efficient data handling

Preprocessing

Intuitive API

NumPy

A numerical computing library for array operations and matrix manipulations. Used in ML for performing fast mathematical computations. Hire developers fluent in both NumPy and Pandas for training, feature engineering, evaluation, and operational reporting work.

Fast computations

Matrix operations

JAX

A high-performance ML framework with strong TPU support and a functional programming model. Hire JAX developers for large-scale training and research-grade work that needs accelerator efficiency.

TPU acceleration

High throughput

Functional design

Hugging Face Transformers

The standard library for transformer-based models across language, vision, audio, and multimodal applications. Hire ML developers experienced with the Hugging Face hub for fine-tuning, deployment, serving, and ongoing evaluation work.

Pre-trained model hub

Fine-tuning ready

Multimodal coverage

OpenCV

The classical computer vision library covering image processing, feature extraction, tracking, and segmentation primitives. Hire OpenCV-fluent developers for vision pipelines that mix classical CV with modern deep learning models.

Real-time CV

Wide algorithm coverage

Mature CV standard

YOLO11

The current Ultralytics object detection model for real-time computer vision work. Hire YOLO11 developers for object detection, segmentation, classification, pose estimation, and oriented bounding box tasks.

Real-time inference

Multi-task model

NMS-free architecture

SAM2

Meta's Segment Anything Model 2 for image and video segmentation. Hire SAM2-experienced developers for object isolation, masking, instance separation, and downstream pipelines that need pixel-level outputs.

Pixel-level precision

Video segmentation

Zero-shot ready

LangChain and LlamaIndex

LLM application frameworks for retrieval-augmented generation, agent orchestration, structured data retrieval, and tool integration. Hire developers fluent in both for LLM apps that need composable retrieval and tool use.

Faster RAG builds

Tool integration ready

Agent orchestration

vLLM

The production LLM inference engine with PagedAttention for high-throughput serving on NVIDIA GPUs. Hire vLLM-experienced developers for cost-efficient serving of open-weight models.

High-throughput serving

Cost-efficient GPUs

PagedAttention engine

Ollama

The local LLM runtime for development workflows and rapid prototyping. Hire developers experienced with Ollama for local-first development and OpenAI-compatible API integration before deploying.

Local-first development

Quick prototyping

OpenAI-compatible API

OpenAI & Claude APIs

The dominant closed-API LLMs where model quality justifies the per-call cost. Hire developers integrating both for chat, agentic, generative, and multimodal product features.

Top-tier quality

Production-ready APIs

Multimodal coverage

Open-weight LLMs

(Llama 3, Mistral, DeepSeek, Qwen) The leading open-weight model families ML developers fine-tune and deploy for inference. These models fit use cases where data privacy, cost, fine-tuning control, or response quality matters.

Data privacy control

Lower inference cost

Custom fine-tuning

Image & speech

FLUX, Stable Diffusion 3, Whisper, and CLIP. The current generative and multimodal models for image synthesis, speech transcription, vision-language search, and content moderation work. Hire developers experienced with each for fine-tuning and integration into product workflows.

Current 2026 models

Production-grade quality

Multimodal coverage

Spark

A distributed data processing framework that enables scalable ML model training on large datasets. It provides parallelized data transformations and supports MLlib for machine learning tasks. Hire Apache Spark developers for large-scale batch and streaming ML feature work.

Big data processing

Scalable ML

Distributed computing

Kafka

A real-time event streaming platform that allows ML models to process continuous data streams. ML developers use Apache Kafka for real-time anomaly detection and predictive analytics.

Real-time

Scalable pipelines

Event-driven ML

Ray

A distributed computing framework for parallel ML training and inference across multiple nodes. Hire Ray-experienced developers for reinforcement learning, distributed training, large-scale serving, and parameter optimization workloads.

Distributed training

GPU-optimized

Reinforcement learning ready

Dask

A Python-native parallel computing library for ML feature engineering on large datasets. Hire Dask developers for parallel processing of training datasets that exceed single-machine memory.

Python-native parallelism

Memory-efficient

Pandas-compatible API

Kubeflow

A Kubernetes-native ML pipeline platform for training and serving infrastructure. Hire Kubeflow-experienced developers when your team owns its ML infrastructure on Kubernetes.

Kubernetes-native

MLOps automation

Open-source flexibility

years of usage

MLflow

The standard open-source experiment tracking and model registry tool. Hire MLflow-experienced developers for ML pipeline management.

Experiment tracking

Built-in model registry

Wide framework support

What machine learning developers can build for your business

Computer vision systems

Predictive analytics

Recommendation systems

NLP and language

Generative AI

MLOps and deployment

Anomaly detection

Computer vision systems

Computer vision engineers from the DOIT network specialize in object detection, image segmentation, video analytics, and vision-language models. They help businesses automate visual processes across security, healthcare, defense, and manufacturing workflows.

✔ Object detection and image segmentation
✔ OCR and intelligent document processing
✔ Video analytics for motion detection and tracking
✔ Vision-language search and multimodal retrieval

Predictive analytics and forecasting

Forecasting specialists from the DOIT network build demand forecasting, churn prediction, predictive maintenance, and risk modeling systems. They help businesses use historical data to anticipate revenue and attrition alongside operational risk patterns.

✔ Time-series forecasting for demand and revenue
✔ Customer churn and lifetime value modeling
✔ Predictive maintenance and equipment failure detection
✔ Risk modeling for finance and insurance

Recommendation systems

Recommendation systems engineers specialize in ranking models, behavioral personalization, content engines, and dynamic pricing systems. The DOIT network places specialists who help e-commerce and media businesses increase engagement and conversions through smarter content delivery.

✔ Product and content recommendation engines
✔ Personalized search and ranking algorithms
✔ Dynamic pricing and user behavior modeling
✔ Context-aware recommendation pipelines

NLP and language understanding

NLP engineers from the DOIT network work in text classification, semantic search, entity extraction, and language model fine-tuning. They help businesses extract value from unstructured text and customer conversations.

✔ Text classification and named entity recognition
✔ Semantic search and vector retrieval
✔ Multilingual translation and analysis
✔ Sentiment and intent detection

Generative AI applications

Generative AI engineers build LLM applications with fine-tuning, RAG pipelines, agentic workflows, and generative media integration. The DOIT network places specialists who turn open-weight and closed-API language models into real product features.

✔ Fine-tuned open-weight LLM applications
✔ Retrieval-augmented generation pipelines
✔ Agentic workflows with tool use and orchestration
✔ Generative image, audio, and video integration

MLOps and model deployment

MLOps engineers specialize in training pipelines, model registries, serving infrastructure, and monitoring. The DOIT network places specialists who keep ML models reliable across scaling, retraining, observability, and drift detection work.

✔ Training orchestration and scheduled retraining
✔ Model registries and version management
✔ Production monitoring and drift detection
✔ A/B testing and gradual rollout infrastructure

Anomaly detection

Anomaly detection specialists work across financial systems, cybersecurity events, IoT sensor streams, and operational signals. The DOIT network places engineers who help businesses spot fraud, breaches, operational outliers, and risk patterns as they happen.

✔ Financial fraud detection in real time
✔ Cybersecurity threat detection and triage
✔ IoT anomaly detection across sensor streams
✔ Production monitoring with anomaly alerting

Dallas, TX, United States

Availability:

Full-time

Tech stack: Python, NumPy, Pandas, OpenCV, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, SpaCy, MLflow, FastAPI, Docker, Git

A machine learning engineer with 4 years of commercial experience across NLP and computer vision projects. Most recent work was building a content-classification system for an EdTech platform that tags user-generated text with intent and topic categories then routes the output into a downstream personalization engine. Has deployment-side experience as well, shipping inference services through FastAPI and managing model versions with MLflow.

Request CVs

Availability:

Full-time

Request CVs

Explore 715+ ML developers for hire in the DOIT talent network

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What Do Clients Say About DOIT Software?

Kjell Garatun-Tjeldstø

CEO

Jarbtech Solution Group

DOIT Software's efforts increased the business's throughput, allowing the internal team to focus on other processes. They have strong communication skills and managed to meet project deadlines despite the tight timeline.

Gil Dror

CTO

Human Care Systems

Their knowledge, diligence and proactivity stand out the most. They are highly productive and demonstrate excellent communication, teamwork, and architecture skills. They are very knowledgeable about best practices and design methodologies, so they are often called upon to answer questions.

Larissa Paschyn

Founder

Citizens to the Rescue

Despite my lack of coding experience, they were able to take my requirements into account and turn them into a functional, well-designed application. I was very impressed with their work and it has already received a lot of positive feedback for its ease of use. I appreciated how open and transparent they were in their work.

Dean Dzurilla

Product Manager

Visible Impact

DOIT Software understands that their business is more than just writing code. They go the extra mile to make sure they meet their customers' needs. They are driven by a desire to help their clients succeed at all costs.

FAQ about hiring ML developers

Where can I hire machine learning developers?

You can find ML developers on freelance platforms and job boards or equip verified remote experts from DOIT Software. DOIT has a network of 715+ vetted machine learning engineers from the USA, Canada, Mexico, Argentina, Brazil, Poland, Czechia, Bulgaria, Ukraine, and Romania available for full-time and part-time roles.

What skills should I look for in machine learning developers?

Look for Python programming skills alongside modern ML frameworks like PyTorch, TensorFlow, scikit-learn, or JAX, plus hands-on experience training and deploying models in production. MLOps experience and stack-specific tools such as OpenCV, Hugging Face Transformers, MLflow, and Weights & Biases matter when the role involves a specific ML domain.

What is the hourly rate for an ML developer?

With DOIT, you can hire machine learning developers with the needed level of expertise in the location of your choice to better fit your budget. In the US, ML engineers charge an average of $80 to $150+ per hour, while rates for skilled Eastern European and LATAM talent range from $50 to $70 per hour.

What types of projects can I hire machine learning developers for?

You can hire machine learning developers for computer vision systems, predictive analytics, recommendation engines, NLP applications, generative AI products, MLOps platforms, and anomaly detection work. The match depends on the hands-on ML engineering skills your project actually needs.

How long does it take to hire machine learning developers?

Hiring timelines depend on the role's ML domain, seniority level, stack rarity, and market availability. Typically, DOIT recruiters find the first relevant ML developers in widely used technologies in 3-5 business days and complete hiring within 2-4 weeks. If your tech stack is rare, the DOIT team will work with you to provide a realistic timeline and keep you informed throughout the search process.

How do I hire a machine learning engineer with DOIT?

You share your role brief, DOIT filters the talent network and returns pre-screened profiles, you interview the ML developers you want to meet, and DOIT handles contracts and onboarding. The hiring team supports you through every step of the placement.

What types of machine learning developers can I hire?

DOIT places machine learning engineers, applied ML engineers, deep learning engineers, computer vision engineers, MLOps engineers, LLM engineers, and generative AI engineers. The team matches developers against the ML domain your role calls for.

What if I'm not satisfied with a machine learning developer?

DOIT delivers a free replacement and supports knowledge transfer so the project keeps moving. The talent guarantee covers the full engagement period, with the hiring team working through the issue alongside you.

Hire Machine Learning Developers | Top 1.5%

Hire machine learning developers in 5 steps

Share your role brief

Get matched profiles

Review ML developers

Interview the best

Onboard with full support

Why hire machine learning engineers with DOIT

How DOIT vets machine learning developers

Experience review

Technical vetting

Fit confirmation

Hire the right ML developer for your project

Machine Learning Engineer

Applied Machine Learning Engineer

MLOps Engineer

Deep Learning Engineer

Computer Vision Engineer

LLM Engineer

Generative AI Engineer

Hire machine learning developers with advanced tech skills

Machine Learning

Deep Learning and Computer Vision

Generative AI and LLM

MLOps

What machine learning developers can build for your business

Top 1.5% of machine learning engineers for hire

Oleksii

Alicja

Dimitar

Franco

Colin

What Do Clients Say About DOIT Software?

FAQ about hiring ML developers

Where can I hire machine learning developers?

What skills should I look for in machine learning developers?

What is the hourly rate for an ML developer?

What types of projects can I hire machine learning developers for?

How long does it take to hire machine learning developers?

How do I hire a machine learning engineer with DOIT?

What types of machine learning developers can I hire?

What if I'm not satisfied with a machine learning developer?

Where can I hire machine learning developers?

How long does it take to hire machine learning developers?

What skills should I look for in machine learning developers?

How do I hire a machine learning engineer with DOIT?

What is the hourly rate for an ML developer?

What types of machine learning developers can I hire?

What types of projects can I hire machine learning developers for?

What if I'm not satisfied with a machine learning developer?

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