6 months ago
Base Salary
$151k - $178k/yr
Responsibilities
- Build in-house production systems that ingest plant telemetry, live data feeds, event logs, quality data, maintenance history, and operational context.
- Reconstruct equipment and process behavior from raw data and identify meaningful deviations between expected and actual execution.
- Develop systems for process drift detection, fault classification, and operational risk quantification before failures, downtime, or quality losses occur.
- Build and deploy machine learning models for anomaly detection, fault classification, process monitoring, quality prediction, forecasting, and related manufacturing use cases.
- Develop models connecting recipe conditions, process parameters, equipment behavior, and intermediate results to downstream product quality and performance.
- Build feedforward and feedback models using upstream signals, in-process data, and downstream results to improve execution decisions.
- Apply AI models and agentic workflows where they materially improve engineering execution, diagnosis, knowledge retrieval, or workflow automation.
- Combine deterministic engineering logic, statistical methods, optimization, machine learning, and foundation models into hybrid solutions.
- Convert model outputs into operational logic supporting triage, escalation, intervention, and action.
- Design and deploy production-grade APIs, model services, data pipelines, feature-generation workflows, inference workflows, event detection systems, and internal tools.
- Partner with Manufacturing, Process Engineering, Controls, Quality, Data Systems, and Software teams to connect technical outputs to real plant actions.
- Help define the architecture and roadmap for operations intelligence across manufacturing and adjacent factory workflows.
Requirements
- Bachelor’s, Master’s, or PhD in Engineering, Computer Science, Operations Research, Industrial Engineering, or a related technical field.
- Strong Python programming skills and experience building production-quality software, internal applications, or data products beyond notebooks and dashboards.
- Experience with scientific computing and machine learning libraries such as pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, TensorFlow, and XGBoost, or equivalent tools.
- Experience building and deploying software services, APIs, data pipelines, or internal platforms using technologies such as FastAPI, Flask, SQL, Spark, Airflow, and dbt, or similar technologies.
- Experience working with time-series, sensor, event, equipment, MES, historian, quality, or other industrial data.
- Experience training, validating, and deploying custom models for prediction, classification, anomaly detection, forecasting, optimization, or control-related use cases.
- Strong systems thinking and the ability to translate ambiguous plant problems into robust technical solutions.
- Experience taking technical systems from concept to deployment with measurable real-world impact.
- Strong written and verbal communication skills and the ability to work effectively across technical and operational teams.
- Preferred experience connecting process conditions or recipe parameters to downstream quality or product performance outcomes.
- Preferred experience with predictive maintenance, process monitoring, fault analysis, quality prediction, or root-cause analysis in industrial settings.
- Preferred familiarity with MES, historians, plant systems architecture, or controls-adjacent environments.
- Preferred experience with sequence modeling, multivariate analysis, optimization, simulation, or hybrid physics and data-driven approaches.
- Preferred experience using LLMs or agentic systems in practical engineering or operational contexts; manufacturing experience is a plus.
Benefits
- On-site role.
- Base pay range of $151,000 to $177,500, with additional benefits, perks, and potential equity as part of the Total Rewards package.
- Equal opportunity employer committed to an inclusive workplace.
Tech Stack
Apache AirflowApache SparkdbtFastAPIFlaskNumPyPandasPythonPyTorchscikit-learnSciPySQLTensorFlowXGBoost
Categories
About Sila
Sila provides developer APIs for U.S. money movement and compliance, including ACH, RTP, and FedNow payments, digital wallets, KYC/KYB, and card acquiring. Fintech companies and software platforms use its sandbox, bank partnerships, and compliance tooling to embed payments and custody features in their apps. Founded in 2018 and headquartered in Portland, Oregon, Sila is a privately held financial technology company.