Profiles in ~48 hoursVetted shortlist after your brief
Start within a weekAfter a short interview or trial
You own the codeIP assignment and NDA
Scale with 30 days noticeAdd or release engineers
Trusted by founders and enterprises worldwide
About hiring Machine Learning engineers
Everything to know before you hire Machine Learning talent
Hire machine learning engineers who treat a model as one component of a system. Speqto places engineers who build feature pipelines, train and validate models, deploy them behind reliable services and watch them after launch, for teams whose data science work keeps stalling before production.
Many ML projects fail between a promising notebook and a live endpoint. A machine learning engineer closes that gap: reproducible training code, versioned datasets, a feature store or at least consistent feature logic between training and serving, and a deployment path that a normal release process can handle. Teams that fix these basics first usually find that model accuracy improves less from new algorithms than from cleaner labels and better features.
The unglamorous work matters most. Training-serving skew, leaking future data into training sets and unlabelled edge cases quietly ruin models that looked excellent offline. Engineers who have been burned by these problems design for them early. A good engineer also writes down what the model must never do, such as using protected attributes or relying on a field that is unavailable at prediction time, and tests for it.
Skills and vetting
We screen for Python depth, solid statistics, and experience with scikit-learn, XGBoost or LightGBM for tabular problems and PyTorch for deep learning. Then we move to engineering: pipeline orchestration with Airflow or Dagster, experiment tracking with MLflow, containerized serving and CI for model code. We ask candidates to critique a model they shipped, including what they would change, because honest post-mortems say more about ability than a list of libraries.
Candidates should be able to pick a metric that matches the business cost of errors, explain calibration, and describe how they would detect drift. Practical questions about class imbalance, cold-start recommendations or forecasting seasonality quickly separate practitioners from course graduates. Domain curiosity counts too: forecasting demand, scoring risk and ranking search results each have their own pitfalls, so we try to match prior experience with your problem type.
How engagements run
Typical setups include one engineer embedded with your data team, or a small group covering data engineering, modeling and MLOps. They work in your repositories, with weekly demos showing model results, error analysis and what changed since the last review. The first weeks usually go to data access, a baseline model and an agreed metric, so everyone can see whether later work actually improves on a simple benchmark.
We share profiles within about two business days, sign NDAs on request and assign all IP, including trained models and pipelines, to you. A team can grow or shrink with 30 days notice. Engineers use AI assistants for boilerplate and tests, but modeling choices and data decisions stay under human review. That discipline keeps experiments honest and gives your own staff something they can read, rerun and extend after the engagement ends.
Why teams hire from Speqto
A bench, not a single freelancer
If an engineer is unavailable or the fit is wrong, a replacement comes from the same bench, with knowledge handed over.
Reliable ingestion, cleaning and feature computation shared between training and serving, so offline results match what happens in production.
Model development
Classification, regression, forecasting, ranking and anomaly detection, starting with strong baselines before moving to more complex architectures. We prove each added complexity beats the baseline.
Deep learning
PyTorch models for vision, text and sequence data, including transfer learning when labeled data is limited. Labelling effort is estimated before training starts.
Deployment and serving
Batch and real-time inference behind APIs, containerized and deployed on Kubernetes or managed cloud ML services. Rollouts are staged so a bad model can be reverted quickly.
Monitoring and retraining
Tracking data drift, prediction quality and latency, with retraining triggers and rollback when a new model performs worse.
Experiment management
MLflow or similar tracking so every model can be traced to its code, data version and parameters for audit and reproduction.
Skill set
Skills and tools your Machine Learning hire brings
Matched against your stack during screening, not listed for decoration.
Python
scikit-learn
XGBoost
LILightGBM
PyTorch
TensorFlow
MLflow
AIAirflow
DADagster
KUKubeflow
SASageMaker
VEVertex AI
How we vet
Five filters before you see a profile
You only interview engineers who have already cleared code review, a live problem-solving session and a communication check.
1Role brief matched to our bench
2Code and project review
3Technical assessment
4Live problem solving
5Communication and culture check
How to hire
Hire Machine Learning talent in four steps
A short, low-risk path from your brief to a working engineer. If the first engineer is not right, we replace them.
1
Share your brief
Tell us the role, stack, hours overlap and how long you need the engineer. A 20-minute call is usually enough.
2
Get vetted profiles
In about two business days you receive candidates with assessment notes, code samples and availability.
3
Interview and trial
Speak to the engineers yourself. Start with a short paid trial and continue only if the fit is right.
4
Onboard and ship
Your engineer joins stand-ups and repositories, demos weekly and is backed by a delivery lead.
Included
Every engineer comes with
No hidden extras: the basics of a safe, managed engagement are part of the package.
NDA and IP assignment
Signed before details are shared; everything built is yours.
Weekly progress reports
Plain-language updates and a demo of working software.
Replacement from our bench
If the fit is wrong, a new engineer joins with a proper handover.
Your tools and repositories
Your Git, tickets and chat; we adapt to your process.
Equipment and payroll handled
Hardware, leave and compliance stay with Speqto.
30 days notice to scale
Add engineers or release them as your roadmap changes.
Compare options
Speqto, freelancer or in-house hire
Speqto dedicated engineer
Freelancer
In-house hire
Time to start
About one week
Days, but quality varies
Often two to four months
Vetting
Practical assessment and senior review
You test every candidate
You run the whole process
If someone leaves
Replacement from our bench
You start the search again
Gap until the next hire
Management
Delivery lead plus your direction
Entirely yours
Entirely yours
Scaling
Add or release with 30 days notice
One person at a time
Slow and costly to reverse
IP and NDA
Assigned to you by contract
Depends on the agreement
Standard employment terms
Equipment and payroll
Handled by Speqto
Their own
Yours
FAQ
Hire Machine Learning Engineers FAQs
Straight answers on cost, timelines, IP ownership, team setup and how we work. Cannot find yours? Ask a solutions architect directly.
What is the difference between a data scientist and a machine learning engineer?
A data scientist explores data and designs models. A machine learning engineer makes them run reliably: pipelines, serving, testing and monitoring. Small teams often need an engineer first, because a good model that cannot be deployed produces no value.
Do we need a lot of data to hire ML engineers?
Not always. Tabular problems can work with thousands of rows, and transfer learning helps with images and text. An engineer will assess your data early and tell you honestly if it is too thin or too messy to support the use case.
Can your engineers work with our existing cloud and tools?
Yes. They commonly work with AWS SageMaker, Azure ML, Google Vertex AI or plain Kubernetes, and adapt to your CI and data stack rather than imposing a new one. They also document the setup so your team can run it independently.
How do you handle sensitive training data?
We sign an NDA on request, work inside your environment where required, and follow your access rules. Data stays in your accounts, and all resulting code and models are assigned to you. Access is logged and limited to people who need it.
We use essential cookies to run this site. With your consent we also use analytics and marketing cookies to understand what helps visitors and to improve our services. Cookies Policy
