Machine Learning Engineer
kforceSan Jose (CA)
About the role
Description:
Kforce has a client in San Jose, CA that is seeking a hands-on Machine Learning Engineer to help build and operationalize ML capabilities supporting a learning opportunity-assessment solution. This role will translate ML and analytical strategies into repeatable feature pipelines, ML workflows, model lifecycle processes, and data integrations.
Key Responsibilities:
- Build and maintain feature-engineering and ML pipelines* Develop repeatable workflows for data preprocessing, model training, evaluation, inference, and retraining* Operationalize ML models beyond experimentation into scalable execution processes* Implement model versioning, experiment tracking, monitoring, testing, and reproducibility practices* Develop data ingestion and integration workflows across enterprise environments* Partner closely with Data Scientists and technical stakeholders to move ML solutions from concept to implementation
Requirements:
- Strong hands-on Python and SQL development* Experience building ML and feature-engineering pipelines, beyond traditional ETL/data engineering* Experience with Scikit-learn or comparable ML frameworks* Strong understanding of features, model training, evaluation, inference, and operationalization* Experience with structured and unstructured data processing using Pandas, NumPy, or similar libraries* Experience with model registries, versioning, experiment tracking, monitoring, automated testing, and retraining workflows* Experience with cloud-based ML/data environments, APIs, integrations, and version-control practices* Strong software engineering fundamentals and experience developing production-oriented ML solutions
Preferred Skills:
- MLflow or similar lifecycle tooling, Feature Stores, containerization, cloud ML platforms, NLP/text-processing pipelines, automated ML testing, enterprise data platforms, and large-scale enterprise ML experience* Ideal Profile: A hands-on ML engineer who has successfully moved models beyond notebooks into reliable, repeatable, operational ML workflows Job Type ContractCompensation70 - $80
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
More like this