Hire Machine Learning Engineers

Ideaware places senior machine learning engineers from Latin America on US teams. You see vetted candidates within 48 hours, your pick starts in 8–12 days, and they work your hours. A senior ML engineer costs $6,000–9,000 a month, with payroll, benefits and equipment included.

An ML engineer trains, deploys and monitors models on your own data: recommendations, forecasting, fraud scoring, computer vision. If you plan to build on top of hosted LLMs such as GPT or Claude, you want an AI engineer instead. The FAQ below covers where the line sits.

What a machine learning engineer costs in the US

A US machine learning engineer earns an average base salary of $162,080 in 2026, with reported pay from $70,000 to $318,000, according to Built In’s salary data. Average total compensation reaches $212,022 once bonuses count. For context, the Bureau of Labor Statistics puts the median for data scientists at $120,230 (May 2025).

Benefits add 30% to employer compensation costs in US private industry (BLS, June 2026), on top of any recruiter fee.

Teams that count benefits and fees save 40–60% against a comparable US hire. The developer cost guide breaks the numbers down by role.

The ML stack our engineers work in

Our ML engineers write Python every day and have taken models from a notebook to a monitored production endpoint. Most profiles cover:

• Modeling: PyTorch and Lightning, TensorFlow/Keras, scikit-learn, XGBoost, LightGBM and CatBoost for tabular data, Hugging Face Transformers
• Data: pandas, NumPy, Spark or Dask, SQL, feature stores such as Feast, validation with Great Expectations
• Experiment tracking: MLflow, Weights & Biases, TensorBoard
• Training and deployment: AWS SageMaker, GCP Vertex AI, Azure ML, Kubeflow, Docker and Kubernetes
• Serving: FastAPI, TorchServe, TensorFlow Serving, ONNX Runtime, Triton Inference Server
• Monitoring: data drift and model decay checks, retraining triggers, shadow and A/B deployments

When the bottleneck is the data pipeline rather than the model, start with a data engineer. For the cloud side of MLOps, pair the hire with a DevOps engineer.

How we vet machine learning engineers

We accept about 3% of applicants. ML candidates pass five steps: an async technical assessment, a 90-minute live coding session, an architecture review of a model they shipped, an English and communication interview, and reference checks with former managers.

The ML rounds focus on production work:

• Problem framing: turning a business question into a target, a baseline and a metric that matches the cost of errors
• Features and leakage: building features without leaking the label, and splitting time-series data correctly
• Evaluation: picking precision, recall, AUC or calibration for the case at hand, and checking results across segments for bias
• Training pipelines: reproducible runs with versioned data, tracked experiments and tuned hyperparameters
• Serving: packaging a model behind an API with latency and cost budgets, plus a rollback path
• Monitoring: spotting drift after launch and deciding when to retrain

You interview the finalists yourself and make the call.

ML engineer, AI engineer or data scientist: which hire do you need?

Hire an ML engineer when you need a model trained on your data and kept healthy in production. The neighboring roles solve different problems:

• Features built on GPT, Claude or open-weight LLMs (RAG, agents, chat, document extraction): hire an AI engineer.
• Text classification, entity extraction or search tuned to your domain: hire an NLP engineer.
• Clean, reliable data in a warehouse or lake before any model: hire a data engineer.
• A small team that ships an AI product end to end: look at AI Pods.

Frequently asked questions

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