Development Engineer

Machine Learning & Data ScienceShift: Monday - Friday, standard 1st shift hours Duration: 06 Months Pay range: $83.00 - $90.90/hr. Remote with a preference on local. And if a local candidate is chosen, there may be an onsite requirement.Job DescriptionMachine Learning & Data ScienceExperience building and deploying ML models in production environmentsHands-on experience with time series forecasting (Prophet, ARIMA, or similar)Understanding of hyperparameter tuning, model validation, and experiment trackingFamiliarity with feature engineering and feature store conceptsData Engineering & ScalabilityProficiency converting pandas-based workloads to PySpark for large-scale processingExperience with distributed data processing frameworks (Spark, Dask, or Ray)Ability to optimize data pipelines for performance and cost efficiencyWorking knowledge of data formats (Parquet, CSV) and partitioning strategiesExperience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets)ML Pipeline OrchestrationExperience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or AirflowUnderstanding of pipeline component design, DAG orchestration, and caching strategiesAbility to integrate data validation, model training, and deployment steps into workflowsExperience with pipeline parameterization and configuration managementSoftware EngineeringStrong Python proficiency with production-grade coding standardsAbility to read, refactor, and extend existing codebasesVersion control experience (Git) and structured change managementFamiliarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting)Cloud & InfrastructureHands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platformsFamiliarity with containerization (Docker) and container orchestration (Kubernetes)Experience with CI/CD pipelines for ML workflowsUnderstanding of secrets management and environment configurationTechnical Skills: Nice to HaveExperience with Ray for distributed ML training and inferenceExposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN)Knowledge of ML model monitoring and drift detectionExperience with infrastructure-as-code (Terraform, Cloud Deployment Manager)Familiarity with retail, supply chain, or demand forecasting domainsExperience working with data science teams to productionize research codeBackground in scaling ML systems from prototype to enterprise-grade deploymentsJob Summary This role supports the development and modernization of the demand forecasting capabilities within Client's digital fulfillment organization. The team is responsible for forecasting order volumes, units, and fulfillment capacity across multiple channels (OPU, Ship-to-Home, Drive Up) to optimize store operations planning. Working closely with data scientists and platform engineers, this role bridges ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring forecasting models run reliably at scale. The ideal candidate brings an ML engineering mindset—combining data engineering, pipeline orchestration, and software engineering skills—to modernize a complex forecasting ecosystem that directly impacts store labor planning and customer experience.Employee Benefits:Four options of medical InsuranceDental and Vision Insurance401k ContributionsCritical Illness InsuranceVoluntary Permanent Life InsuranceAccident InsuranceOther Employee PerksAbout LanceSoftLanceSoft is rated as one of the largest staffing firms in the US by SIA. Our mission is to establish global cross-culture human connections that further the careers of our employees and strengthen the businesses of our clients. We are driven to use the power of our global network to connect businesses with the right people, and people with the right businesses without bias. We provide Global Workforce Solutions with a human touch.