For US product and engineering leaders
Hire a Machine Learning developer for US teams: specialists who take models from notebook to production, explained to your team and watched for drift once they’re live.
- Senior engineering oversight
- US workday overlap
- Your stack and controls
Start with one Machine Learning developer. Add a team when the product needs more capacity.
ML capacity, inside your org.
Machine learning engineers who work in your codebase, quality gates and release rhythm.
Built for product and engineering leadersOne senior Machine Learning developer to a full data team
- 01Years engineering software
- 20+
- 02Engineers under one roof
- 200+
- 03Clients served
- 300+
- 04Global delivery locations
- 4+
Dallas headquarteredDelivery across the USA, India, Canada and Mexico
Tools a hired Machine Learning developer uses
We work with the machine learning tools, frameworks and architecture your teams already use: Python, PyTorch, scikit-learn and MLflow.
Core
- PythonLanguage
- PyTorchDeep learning
- scikit-learnClassic ML
- XGXGBoostGradient boosting
- MLflowExperiment tracking
- TensorFlowModel training
Ecosystem
- pandas
- NumPy
- Jupyter
- Weights & Biases
- DVC
- Ray
- ONNX
- FastAPI
- Kubeflow
We choose tools for your product, your data, your security needs and your deployment plan.
ML work we cover
The machine learning work our engineers take on, from models and pipelines through explainability and MLOps, and the tools they use every day.
Models that meet the target
The simplest model that meets your target, measured against an honest baseline, with deep learning only where the data earns it.
Common work
- Model development
- Feature engineering
- Baselines
Tools
- scikit-learn
- XGBoost
- PyTorch
- pandas
Notebook to production
Notebooks turned into versioned pipelines with tracked experiments, tested data and models you can deploy and roll back.
Common work
- ML pipelines
- Experiment tracking
- Model serving
Tools
- MLflow
- DVC
- FastAPI
- Docker
Predictions people can check
Feature importance for the model and a breakdown for any single prediction, in terms your team can explain.
Common work
- Explainability
- Model cards
- Bias checks
Tools
- SHAP
- scikit-learn
- Jupyter
- MLflow
Models that stay accurate
Inputs and outputs watched for drift, with retraining that ships a new model only when it beats the old one.
Common work
- Drift monitoring
- Retraining
- A/B testing
Tools
- MLflow
- Evidently
- Kubeflow
- Python
Why JumpGrowth
Senior India-based engineers for US teams, backed by delivery across the USA, India, Canada and Mexico.
- Years engineering software
- 20+
- Engineers under one roof
- 200+
- Clients served
- 300+
Engineers matched to your work
Start from the ML stack, domain, seniority and delivery habits you actually need. Review profiles and test the technical fit before you choose.
- Specialists
- Domain experience
- Direct interviews
Your team keeps ownership
Your leads set priorities, review the work and own the roadmap. Our engineers join your rituals, your repositories and your release process.
- Your tools
- Your rituals
- Your roadmap
Capacity without the handoff
Nothing is thrown over a wall. Engineers work in your sprints with US workday overlap, so models, pipelines and MLOps move every day.
- US overlap
- Daily stand-ups
- Shared sprints
Machine Learning developer hiring models
Embed a Machine Learning developer in your team, add a small ML squad, or pair ML engineers with a dedicated delivery pod.
Staff augmentationEmbed one Machine Learning developer
A dedicated ML specialist in your team, your codebase and your sprint cycle, reporting to your lead.
- Python
- PyTorch
- scikit-learn
Augmented squadAdd a team of Machine Learning developers
Several Machine Learning developers for a clear gap across models, pipelines and MLOps, led by your own managers.
- PyTorch
- scikit-learn
- XGBoost
Managed delivery podPair ML with a pod
Machine Learning developers with a dedicated delivery pod, so models, data and release stay in one team.
- Python
- Architecture
- Delivery
You stay in control. Our engineers work in your codebase, your tools and your delivery process.
Cost savings calculator
Move the slider to compare a senior US machine learning team with an India-based team of the same size. Price depends on seniority, US overlap and whether you hire one developer or a pod.
A planning estimate: $90 an hour onshore and $32 offshore, 160 hours per person each month. Not a JumpGrowth quote. Final cost follows seniority, overlap and scope.
US onshore at $90/hr
$43,200
per month
Offshore at $32/hr
$15,360
per month
Estimated monthly savings
$27,840
About 64% lower than the US onshore estimate.
Get a costed plan for the Machine Learning role, seniority and team shape you need.
Find the right skill
Pick a skill to see the kinds of engineers we match to your team: ML architect, machine learning engineer, applied data scientist or MLOps engineer.
Senior specialty role
ML Solutions Architect
Sets the shape of your ML system: data, training, serving and the standards the team builds to.
- Python
- PyTorch
- MLflow
- Kubeflow
Senior specialty role
Machine Learning Engineer
Builds models end to end, from the training data to the deployed endpoint, with tests and tracking alongside.
- PyTorch
- scikit-learn
- XGBoost
- MLflow
Senior specialty role
Applied Data Scientist
Frames the business question, explores the data and finds the features and models worth building.
- Python
- pandas
- scikit-learn
- Jupyter
Senior specialty role
MLOps Engineer
Runs training, deployment and monitoring as pipelines, so models ship, roll back and retrain on schedule.
- MLflow
- Kubeflow
- Docker
- Kubernetes
Example roles. We confirm availability and fit before you decide.
What our engineers do
Add the skills you need across models, pipelines, explainability and MLOps.
Build
Build ML models
Machine learning engineers who build forecasting, ranking, classification and vision models that hold up in production.
- PyTorch
- scikit-learn
- XGBoost
Integrate
Connect your data
Specialists who connect models to your data, your product and the decisions they support.
- Feature pipelines
- Model APIs
- Batch scoring
Platform
Deploy securely
Engineers who deploy, monitor and retrain models inside your cloud and under your security rules.
- MLOps
- Drift monitoring
- Security
How they help
Hire one Machine Learning developer or an ML team. We cover models, pipelines, monitoring and production.
- 01
Start from the decision
Turn a business question into a prediction your product can use, with a clear target and a baseline to beat.
Problem, target and baseline
- 02
Prepare data and features
Pipelines that clean, version and test the data, and build the features the model learns from.
Data, features and validation
- 03
Train and deploy
Tracked experiments, honest evaluation, and models served behind an API or a batch job you can roll back.
Training, evaluation and serving
- 04
Monitor and retrain
Drift and accuracy watched in production, with retraining that ships a new model only when it beats the old one.
Drift, retraining and ownership
Need a related team around your Machine Learning developer?
Find the right roleHow teams use us
The roles we brought in, and what each team achieved. Examples of use, not guarantees.
- US product company01
Dedicated product pod
Forecasting model team
We provided machine learning engineers to take a forecasting model from notebook to production and set up reliable retraining.
Capability mix
- ML Solutions Architect
- Machine Learning Engineer
- Delivery Lead
Python · XGBoost · MLflow · FastAPI
- Faster
- model releases
- Reliable
- production forecasts
- US enterprise platform02
Architecture and delivery
ML platform modernisation
We added specialists to move the platform’s models onto versioned pipelines with tracking, monitoring and safe rollbacks.
Capability mix
- ML Solutions Architect
- MLOps Engineer
- Data Engineer
PyTorch · MLflow · Kubeflow · Kubernetes
- Repeatable
- model training
- Reduced
- manual work
- US financial services03
Augmented squad
Explainable risk models
We placed machine learning engineers inside the client’s strict security and compliance workflow to ship risk models the business can explain.
Capability mix
- Machine Learning Engineer
- Applied Data Scientist
- Security Engineer
scikit-learn · XGBoost · SHAP · Python
- Compliant
- model releases
- Explainable
- predictions
Client details are kept private. Roles show the skills each engagement used. Results are examples, not guarantees. Related reading: Case studies, Hiring insights on the blog, How we build teams and Prototype to launch.
One team. Two locations.
Your leaders own the product and the architecture. Our India-based engineers join your way of working, backed by delivery across the USA, India, Canada and Mexico.
- Start small with the Machine Learning developer you need now.
- Work your way in your codebase, tools and quality process.
- Grow when needed by pairing ML engineers with a dedicated delivery pod.
- 01ML Solutions ArchitectArchitecture
- 02Machine Learning EngineerCore delivery
- 03Applied Data ScientistModelling
- 04MLOps EngineerPlatform
- Rituals
- Daily overlap
- Ownership
- Sprint outcomes
- Start
- Roadmap-led
Is this a good fit?
- 01You need a Machine Learning developer to ship models, pipelines and MLOps in your stack.
- 02You want to embed a Machine Learning developer in your team or pair engineers with a dedicated delivery pod.
- 03You need engineers who follow your security and release process.
- 04You want India-based engineers who work closely with your US team.
How it works
Leadership context
Discovery call
Tell us about your product, the ML gap and whether you need an architect, machine learning engineer, applied data scientist or MLOps engineer.
What we cover
- Your goals
- The team gap
- Your ML context
Team shape
Pick the model
Choose one Machine Learning developer, a small ML squad, or a developer paired with a delivery pod, and agree how the team works with yours.
What we cover
- Engagement model
- Seniority mix
- Working hours
Shortlist
Meet vetted options
Within days, review machine learning engineers matched to your stack, your domain and the way you deliver.
What we cover
- Matched profiles
- Evidence of skills
- Availability
Your decision
Interview and select
Interview the shortlist yourself, test the technical fit and choose who joins.
What we cover
- Technical interview
- Team fit
- Your final pick
First sprint
Onboard and kick off
Access, introductions and a walkthrough of the codebase, then the first tickets inside your sprint.
What we cover
- Access and tools
- Codebase walkthrough
- First commit
Step 01Machine Learning case studies / 02
Public products that used speech, voice or model features a hired Machine Learning developer would own. Open a study for the brief, platform and stack.
Machine Learning client stories / 01
Clients whose products sit next to the science and data work a hired Machine Learning developer ships.
They understood the science and the software. That combination is rare, and it shows in the product.
01How do I hire a Machine Learning developer for my team?
Hire a Machine Learning developer to take models from notebook to production, with pipelines, explainability and MLOps. Embed one engineer in your team or add an ML squad. You interview the shortlist and choose who joins.
02What machine learning skills can we hire for?
ML architecture, PyTorch, TensorFlow and scikit-learn, gradient boosting, feature engineering, experiment tracking, model serving, explainability, drift monitoring and MLOps.
03We only have a notebook. Can a hired Machine Learning developer start from that?
Yes. We turn it into a versioned pipeline with tracked experiments, tests on the data, and a model you can deploy and roll back.
04Do we need deep learning?
Often not. We start with the simplest model that meets the target, and reach for PyTorch or TensorFlow when the data earns it.
05How do you explain predictions to the business?
Global feature importance for the model, and a per-prediction breakdown for any single decision your team needs to justify.
06What happens after launch?
Inputs and outputs are watched for drift. When a score crosses its threshold, retraining runs and a new model ships only if it beats the old one.
07Can we embed a Machine Learning developer in our team or hire a pod?
Both. Add one Machine Learning developer through staff augmentation, or pair ML engineers with a dedicated delivery pod when you need models and release in the same team.
08What affects the cost of hiring a Machine Learning developer?
Seniority, US workday overlap, and whether you hire one developer or a pod. The planner on this page uses a $90/hr onshore and $32/hr offshore planning rate, 160 hours a month. It is an estimate, not a JumpGrowth quote.
09Is this staff augmentation or a managed delivery team?
Both. Add one Machine Learning developer, a small team, or a full pod with its own lead.
10Can we start with one Machine Learning developer?
Yes. Most teams start with one Machine Learning developer in a clear gap and add people once the fit is proven.
11Can we interview Machine Learning developers before we choose?
Always. You see the profiles, run your own interviews and make the final call.
12Can we scale the team after the first Machine Learning developer starts?
Yes. Add people as the roadmap grows, or move from one Machine Learning developer to a managed pod with its own lead.
Hire a Machine Learning developer
Tell us whether you need an ML architect, machine learning engineer, applied data scientist or MLOps engineer, and whether they join your team or a pod.