4 months ago
Alameda, CA, USAStaff+
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 is the leading ACH API for fintech developers. Our customers have built industry-leading Fintech applications using our developer-friendly sandbox, bank partnerships, and compliance resources. If your application needs ACH, RTP, FedNow, digital wallets, KYC/KYB, and a card-acquiring solution…we should chat.