Manufacturing Yield & Device Analytics Engineer

We’re pragmatic in our understanding of what the earth needs to move towards a sustainable, high-tech future. It’s our passion, however, that drives everything we do. It’s present in every level of our business and every step of our process. We work to inspire—to show the world there is a better way. And that better way isn’t decades in the future;

with us, it’s now.

We are a team of scientists and engineers with diverse backgrounds, drawing on expertise in chemistry, physics, engineering, mathematics, computer science and automation. We are continuously seeking to grow our team with people who demonstrate outstanding integrity, intelligence, hunger, accountability and the passion for learning. Positions that we are actively hiring for are listed below. However, we will hire exceptional individuals irrespective of our current job openings.

Manufacturing Yield & Device Analytics Engineer

San Marcos, TX

• Drive Yield Improvement:
  Utilize data analytics and statistical methodologies to proactively identify process variation, pinpoint production inefficiencies, and execute targeted yield improvement and waste reduction opportunities.
• Statistical Performance Tracking:
  Analyze high-volume manufacturing data trends, SPC metrics, scrap rates, equipment downtime, cycle times, and overall yield performance to spearhead continuous improvement initiatives.
• Lead Advanced DOE:
  Direct complex Design of Experiments (DOE) activities to establish robust process windows, stabilize process robustness, and achieve challenging process capability targets.
• Predictive Performance Modeling:
  Develop predictive and preventive improvement strategies through analytical modeling and process performance evaluation.

• Define Manufacturing Design Rules:
  Deliver precise process capability numbers and statistical performance metrics to Design Engineering to help define, refine, and enforce realistic design rules for optimal manufacturing scaling.
• Cross‑Functional
  
  Collaboration:
  
  Collaborate cross‑functionally with Production, Quality, Supply Chain, and Operations teams to instill a culture of rigorous, data‑based decision‑making across the organization.
• Root Cause Diagnostics:
  Apply advanced root‑cause analysis methodologies (including 8D, 5 Whys, Fishbone, Pareto Analysis, and full CAPA implementation) to diagnose sudden process disruptions or quality events.

• Quality & Regulatory Compliance:
  Ensure all yield‑enhancement initiatives and process changes conform to company Quality Management Systems (QMS), supporting ongoing compliance with ISO 9001 and automotive standards.
• Support Digitization & Industry 4.0:
  Support manufacturing digitization, automation, and smart factory initiatives to improve process consistency and unlock deep operational visibility.
• Equipment Capacity Evaluation:
  Evaluate manufacturing equipment performance, capacity limitations, tool utilization, and baseline process efficiency.
• Product Cost Analytics:
  Conduct thorough product cost analyses including labor touch‑time, machine utilization, scrap generation, cycle time, and process‑related cost drivers.

• Education:
  
  Bachelor’s degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, Electrical Engineering, Materials Science, or a highly related technical field.
• Experience:
  
  3–7+ years of experience in high‑precision manufacturing, process engineering, device engineering, or yield analysis environments.
• Analytical Mastery:
  Exceptional analytical, statistical modeling, and data‑interpretation skills.
• Statistical Tooling Background:
  Deep hands‑on experience utilizing SPC, process capability analysis, complex DOE, and statistical problem‑solving methodologies.
• Root Cause Expertise:
  Highly proficient in rigorous root cause analysis and corrective/preventive action implementation.
• Core Manufacturing Fundamentals:
  Strong working knowledge of manufacturing systems, production optimization, KPI tracking (yield, scrap, OEE, throughput), and Lean/Six Sigma principles.