KLA-Tencor Off Campus Drive 2021: BE/B.Tech | ME/M.Tech | Freshers | AI&ML Engineer | Across India

KLA- Tencor Software Private Ltd Off Campus Drive for Freshers as well experienced 0-3 Year of Engineering of Computer Science, IT, Electronics, Electrical, Mechanical of 2018, 2019, 2020, 2021 Batch throughout across India for the AI and ML Engineer Position for the Chennai Office Location. The interested Graduates find the details about KLA off campus drive in job description.

KLA-Tencor Off Campus Recruitment Drive 2021

Job Title: AI& ML Engineer

Job Location: Chennai/Remote

Experience: Freshers/0-3 Year

Batch: 2018-2021

Salary: 22 – 30 LPA

KLA Off Campus Description:

KLA Overview:

KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and in 2019 we invested 15% of sales back into R&D.

Eligibility:

• 2018/19/20/21 batch BE/B.Tech/ME/MTech (CS/IT/EC/Electrical/ME)
• Great academic track record (7.5 GPA / 75% throughout)

Job Description:

KLA is currently hiring for 2 roles:

AI Algorithm Engineer

• Develop DL/ML algorithms for world-leading semiconductor inspection platforms
• Combine conventional computer vision and image processing with DL
• Explore and implement linear and non-linear optimization techniques
• Contribute to mission critical, production quality optical inspection algorithms

AI HPC Engineer

• Accelerate Al algorithm performance to advance technology & innovation worldwide
• Passion to theoretically model Al performance & apply parallel computing
• Implement device parallelism on various parallel HW architectures (eg GPUs)
• Optimize most challenging Al workloads using SW frameworks (eg. CUDA)

Apply