Senior Machine Learning developers, based in the US, working your hours
Models, pipelines, and AI features, built by US engineers who take ML from notebook to production.
- Every engineer lives and works in the US
- Works your hours, joins your standups
- NDA and full IP assignment from day one
Book your call: tell us what you need
"NextGen scaled us from 2 to 7 dedicated engineers in under a quarter. Enterprise-grade capacity, zero agency drag."
"Their R&D tax credit workflow platform paid for itself in the first filing season."
"AI-native from day one. Our IRS transcript pipeline runs 24/7 without a human in the loop."
"Property tax appeals used to take weeks. Their comp analysis tooling turned it into hours."
"The AI intake agent handles our tier-1 patient calls with a hand-off cleaner than our old call center."
"POS, inventory, ordering — all unified. We finally have one source of truth across every location."
"The procurement platform paid for itself in the first quarter of use."
"They shipped our matching engine in 6 weeks. Our old team would've taken 6 months."
"Enterprise-grade delivery without the enterprise-agency price tag."
"Their team plugged into our sprint on day one. Zero ramp time."
"NextGen scaled us from 2 to 7 dedicated engineers in under a quarter. Enterprise-grade capacity, zero agency drag."
"Their R&D tax credit workflow platform paid for itself in the first filing season."
"AI-native from day one. Our IRS transcript pipeline runs 24/7 without a human in the loop."
"Property tax appeals used to take weeks. Their comp analysis tooling turned it into hours."
"The AI intake agent handles our tier-1 patient calls with a hand-off cleaner than our old call center."
"POS, inventory, ordering — all unified. We finally have one source of truth across every location."
"The procurement platform paid for itself in the first quarter of use."
"They shipped our matching engine in 6 weeks. Our old team would've taken 6 months."
"Enterprise-grade delivery without the enterprise-agency price tag."
"Their team plugged into our sprint on day one. Zero ramp time."
Sound familiar?
- Hiring a senior Machine Learning developer full-time is taking months.
- Your Machine Learning codebase has grown messy and every change breaks something else.
- Offshore hand-offs and time-zone gaps turn one-day fixes into week-long threads.
- You need someone senior enough to make decisions, not just close tickets.
What our Machine Learning engineers build
Production ML
Models deployed, monitored, and retrained on schedule.
LLM & AI features
Search, assistants, and automation built on modern models.
Data foundations
Training data and features cleaned and organized.
How it works
- 01
15-minute call
Tell us what you need built or fixed and where it's stuck.
- 02
Matched profiles
You meet US-based engineers with directly relevant work behind them.
- 03
You interview
Run your own technical interview. You only move forward with people you pick.
- 04
They start shipping
Onboarded into your repo, tools, and review process, with a weekly written update.
Hire US-based machine learning developers from NextGen Coding Company for Python data pipelines, model evaluation, and production inference work. Define the role around your workload: scikit-learn classification, PyTorch 2.x training, XGBoost ranking, or Hugging Face Transformers inference. A useful brief identifies the available labels, prediction target, deployment environment, and cost of incorrect predictions before selecting a model family or proposing a training run.
Machine learning projects often stall between a promising notebook and a service that behaves reliably with new data. Scope engineering work around reproducible preprocessing, leakage-resistant evaluation, MLflow experiment tracking, and FastAPI endpoints with input validation. Include latency budgets, drift checks, and rollback expectations in the plan. Our engineers are senior, many with 10+ years of experience, and work your hours while participating in your standups.
Frequently asked questions
- Should I hire a machine learning developer or a data scientist?
- Hire for the work rather than the title. If you need experimental design, target definition, and statistical analysis, emphasize those skills in interviews. If your bottleneck is packaging models, building inference APIs, or maintaining training pipelines, prioritize software engineering and deployment experience. Some projects need both skill sets with clearly divided responsibilities.
- When should a project use scikit-learn instead of PyTorch?
- Scikit-learn is a practical starting point for many tabular classification and regression tasks, especially when you need inspectable baselines and consistent preprocessing. PyTorch fits workloads requiring neural architectures or custom training loops. Benchmark appropriate alternatives, including XGBoost for tabular data, against your evaluation criteria before accepting additional infrastructure and tuning complexity.
- How do machine learning developers prevent data leakage?
- Split data according to how predictions will be used, which may require time-based or entity-based partitions rather than random rows. Fit preprocessing only on training data, using tools such as scikit-learn Pipeline. Check for post-outcome fields, duplicate records, and related entities crossing splits. Keep final evaluation data separate from model selection.
- What is needed to deploy a machine learning model in production?
- Deployment needs more than a serialized model. Package preprocessing and dependencies, validate input schemas, and version the artifact alongside its evaluation results. FastAPI and Docker can support an inference service, while MLflow can track experiments and artifacts. Monitor latency, errors, and data changes; assess prediction quality when reliable outcome labels become available.
- How do I hire a US-based machine learning developer without sharing code first?
- Start with a 15-minute call describing your dataset structure, model stack, and deployment constraints without exposing confidential records. NextGen Coding Company signs an NDA and full IP assignment before touching code. All engineers live and work in the US. Most teams interview matched engineers within a week of the first call.
- Are your developers really based in the US?
- Yes. Every engineer lives and works in the United States. No offshore subcontracting and no hand-offs to another team. That's written into the contract.
- How fast can someone start?
- Most teams interview matched engineers within a week of the first call. Start dates depend on your interview process.
Related reading
- How to hire a software developer
What to check before you sign.
- Hiring test for senior engineers
How to vet senior talent fast.
- Senior engineer salary guide 2026
What the market pays.
- Onshore vs offshore vs nearshore
The honest tradeoffs.
- Forward deployed engineers
Need an owner, not just capacity?
- Resource library
Every guide and benchmark we publish.
Start your project request
Tell us what you're building — engineering capacity, AI, QA, cloud, or a fixed-scope software engagement. Our NYC team responds within one business day.
- Response within 1 business day
- 30-minute discovery conversation
- Recommended engagement model & pricing
- NYC-focused — in-person available
Inbound sales only. All form information is encrypted in transit.