Hire Remote Machine Learning Engineers

Hire Senior ML Engineers Who Take Models From Notebook to Production

A data scientist who can train a model in a Jupyter notebook is not an ML engineer. A machine learning engineer takes that model, hardens it, scales it, serves it at low latency, monitors it for drift, retrains it when performance degrades, and integrates it into a product that millions of users interact with. That’s an entirely different skill set — and it’s the one that actually creates business value.

Our ML engineers have built the recommendation systems, fraud detection models, NLP pipelines, and computer vision systems that drove 8-figure business outcomes at companies like Google, Amazon, Microsoft, Mercado Libre, and Grab — and at unicorn startups that reached billion-dollar valuations based on the ML systems these engineers built.

What Our ML Engineers Build

Recommendation & Personalization Systems

Collaborative filtering, content-based recommendation, two-tower models, and real-time ranking systems. Our ML engineers have built recommendation systems that drove significant revenue increases for e-commerce, streaming, and marketplace platforms — by showing the right content to the right user at the right moment.

Natural Language Processing (NLP) Systems

Text classification, named entity recognition, sentiment analysis, semantic search, question answering, and document understanding. Our ML engineers build NLP pipelines using Hugging Face Transformers and fine-tune domain-specific models that outperform general-purpose LLMs on specialized tasks.

Computer Vision Systems

Object detection, image segmentation, video analysis, OCR, and anomaly detection. Our ML engineers have deployed computer vision systems for manufacturing quality control, retail analytics, medical imaging, and autonomous systems — where model accuracy and inference speed are both business-critical.

Predictive Analytics & Forecasting

Time-series forecasting, demand prediction, churn modeling, credit scoring, and fraud detection. Our ML engineers combine classical ML (XGBoost, LightGBM) with deep learning approaches to build predictive systems that match the right methodology to the problem.

Model Fine-Tuning & Customization

LoRA, QLoRA, and full fine-tuning of LLMs and vision models on domain-specific datasets. Instruction fine-tuning, RLHF, and DPO for behavioral alignment. Our ML engineers fine-tune foundation models that outperform the base model on specialized tasks — with proper evaluation frameworks that prove it.

ML Technology Stack

Frameworks: PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, LightGBM

NLP: Hugging Face Transformers, PEFT, TRL, spaCy, NLTK, sentence-transformers

Computer Vision: OpenCV, Detectron2, YOLOv8/v11, SAM, CLIP, Torchvision, Albumentations

Feature Engineering: Feast, Tecton, pandas, Polars, NumPy

Experiment Tracking: MLflow, Weights & Biases, Neptune, ClearML

Model Serving: TorchServe, TF Serving, vLLM, Triton Inference Server, ONNX Runtime, BentoML

Data: Apache Spark (PySpark), Dask, Ray, Apache Airflow, dbt

Cloud: AWS SageMaker, GCP Vertex AI, Azure ML, Lambda (GPU), Databricks

Client Success Story: Real-Time Fraud Detection for a Global Payment Processor

A global payment processor was losing millions annually to fraud that its rule-based detection system couldn’t catch — without generating a false positive rate that frustrated legitimate customers. The business needed a model that could score transactions in under 10ms while dramatically improving detection accuracy. Our ML engineers built a gradient boosting ensemble trained on hundreds of behavioral and transactional features, deployed with a custom feature store for real-time feature computation and sub-8ms p99 scoring latency. The new system detected 340% more fraud than the rule-based predecessor at a false positive rate of 0.08%. Fraud losses dropped by $18 million in the first year. The model has been retrained monthly since deployment with no degradation in detection performance.

Client Success Story: Personalized Recommendation System for a 20-Million-User Marketplace

A marketplace with 20 million active users was serving the same trending items to every visitor — a missed revenue opportunity the business estimated at tens of millions annually. Real-time personalization at scale was the challenge: recommendations needed to generate within 50ms for a user base with highly variable behavioral signals. Our ML engineers built a two-tower neural network recommendation model trained on implicit feedback — clicks, saves, add-to-carts, purchases, and session duration — served via a TorchServe inference cluster with a Redis approximate nearest neighbor index for candidate retrieval. A/B test results over 30 days showed a 31% increase in items viewed per session and a 19% increase in purchase conversion rate, generating tens of millions in incremental annual revenue directly attributable to the recommendation system.

Why Companies Choose Our ML Engineers

• Production ML discipline: They know the difference between a model that performs in a notebook and one that performs in production — and they build the latter from the start
• Evaluation-first methodology: Before they train, they define the evaluation framework. Before they deploy, they validate against held-out data. Before they recommend a model, they prove it outperforms the baseline
• Full ML stack ownership: Feature engineering, training, evaluation, serving, monitoring — our ML engineers own the entire ML lifecycle, not just the modeling layer
• Classical + deep learning: The right model for the problem, not the most exciting model. Sometimes XGBoost outperforms a transformer — our engineers know when and why
• 50% cost savings: Fully-burdened rates including salary, benefits, taxes, and HR

Engagement Models

• Individual ML Engineer — A senior ML engineer embedded in your existing data science or AI team. Ideal for productionizing models that are stuck in notebooks, building evaluation frameworks for existing AI features, or adding specialized expertise (NLP, computer vision, recommendation systems) to a team that has general ML coverage.
• ML Development Pods (ML engineer + data engineer + MLOps) — A cross-functional squad covering the full ML lifecycle: feature engineering, model training, evaluation, serving, and monitoring. Common for teams launching their first production ML system or rebuilding an existing ML pipeline that has accumulated significant technical debt.
• Full ML Teams (5–15+ engineers) — Complete machine learning organizations for companies where ML is a core product differentiator — covering multiple model domains, a shared feature store, a unified training platform, and systematic model governance.
• Contract-to-Hire ML Engineers — Evaluate an ML engineer’s production discipline on your actual training and serving infrastructure before committing. The quality of their evaluation methodology and feature engineering judgment becomes apparent quickly.

How To Vet ML Engineers

Our ML vetting focuses on production ML discipline — not research performance. The four-stage process:

1. Technical screening — Classical ML (gradient boosting, regularization, cross-validation, class imbalance handling), deep learning (transformer architectures, fine-tuning strategies, LoRA/QLoRA), evaluation methodology (held-out sets, stratified splitting, leakage detection), and MLOps fundamentals (feature stores, model registries, serving patterns, drift detection). Over 90% of applicants do not pass this stage.
2. Modeling challenge — Given a realistic dataset and business objective, design and implement a complete ML pipeline: data exploration and feature engineering, baseline model selection with rationale, hyperparameter optimization, evaluation against a held-out test set, and a documented analysis of failure modes. Evaluated on evaluation rigor, feature engineering creativity, and the quality of uncertainty quantification.
3. Live technical interview — System design for a production ML system (training pipeline, feature store, serving architecture, monitoring and retraining triggers), debugging a given model’s performance failures from evaluation artifacts, and discussion of when a simple classical model outperforms a complex deep learning approach.
4. Communication and stakeholder screening — ML engineers present model performance to product teams and executives who need to make business decisions. We screen for the ability to translate precision/recall trade-offs, confidence intervals, and model limitations into business-relevant recommendations.

What to Look for When Hiring ML Engineers

Strong ML engineers build evaluation frameworks before they build models — because a model without a rigorous evaluation is just a hypothesis. They’ve hit data leakage in production, class imbalance at scale, and concept drift in deployed models, and they have systematic approaches to each.

What strong candidates demonstrate:

• They design evaluation frameworks before they start training: held-out test sets with temporal splits for time-series data, stratified splits for imbalanced classes, and explicit leakage detection checks — because every evaluation shortcut taken in development shows up as production failure
• They have genuine intuition about when to use classical ML vs. deep learning: XGBoost for tabular data with moderate samples, LightGBM for large tabular datasets where interpretability matters, and transformers specifically when the problem requires the inductive biases they provide
• They instrument their models with proper drift detection from day one: feature distribution monitoring (population stability index), prediction distribution monitoring, and business metric correlation — because model degradation in production is silent without explicit monitoring
• They can explain their model’s uncertainty: they know when to return a confidence score, when to abstain, and how to calibrate model probabilities so that “90% confidence” actually means something

Red flags to watch for:

• Not checking for data leakage — candidates who don’t describe explicit leakage checks (temporal splits, feature timestamp validation) may have shipped models that appeared to perform well in evaluation and failed immediately in production
• Treating model accuracy as the primary metric without discussing class imbalance, calibration, or the business cost of different error types
• No experience with concept drift — candidates who’ve only deployed models in stable environments haven’t managed the full ML production lifecycle
• Applying deep learning by default to every problem — candidates who reach for transformers for structured tabular data may be optimizing for the technique they know best rather than the one that fits the problem

Interview questions that reveal real depth:

• “Walk me through how you’d detect and handle data leakage in a classification model that’s predicting customer churn. What specific checks would you build into the pipeline?”
• “A recommendation model’s offline metrics look excellent, but engagement metrics in the A/B test are flat. What are the likely failure modes, and how would you diagnose each?”
• “You’re deciding between a gradient boosting model and a fine-tuned transformer for a text classification task with 50,000 training examples. Walk me through your decision framework.”

Related Services

• Hire AI Developers — The application engineers who build AI-powered products on top of the models our ML engineers create.
• Hire MLOps Engineers — The infrastructure specialists who keep ML systems running reliably in production.
• AI Engineers & ML Engineers — Explore our full AI and ML engineering practice.
• Data Scientists & Data Engineers — The data professionals who build the pipelines and features that feed your ML models.

Want to Hire Remote ML Engineers?

We specialize in sourcing, vetting, and placing senior remote ML engineers — from individual model specialists who build evaluation frameworks before they train and detect concept drift before it affects business metrics, to complete ML organizations covering recommendation systems, fraud detection, NLP pipelines, fine-tuned LLMs, and computer vision at scale. We make it fast, affordable, and low-risk.

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Ready to hire ML engineers who build evaluation frameworks, eliminate data leakage, and monitor for concept drift — not just tune hyperparameters? Contact us today and we’ll introduce you to senior ML engineers within 48 hours.