65 Design Lead jobs in Kochi

Responsibilities:You should be a verification engineer with a knowledge of So C integration verification, So C scenario verification, So C performance verification, CHI/PCIe/CXL, DDRx/LPDDRx integration verification in So C RTL.Project experience with ARM based ecosystem components (A-series ARM Cores, SMMU, GIC, Core sight, NIC and other complex bus interconnects)Your key responsibilities will include writing test plans, defining test methodologies, developing System Verilog/Verilog testbenches and tests, and debugging of test failures and issues.Working with project management and leads on planning tasks, setting schedules, and quality checkpoints.Collaborate with engineers from design & performance analysisRequired Skills and Experience :Proven understanding of digital hardware design and Verilog/System Verilog HDLExperienced in one or more of various verification methodologies – UVM/OVM, formal, power aware verification, emulationExposure to all stages of verification: requirements collection, creation of verification methodology plans, test plans, testbench implementation, test case development, documentation, and supportGood Problem Solving and Debugging skills.“Nice To Have” Skills and Experience :Knowledge of So C Verification Flow and strategy.Experience with ARM-based designs and/or ARM System Architectures.

Responsibilities :

• You should be a verification engineer with a knowledge of SoC integration verification, SoC scenario verification, SoC performance verification, CHI/PCIe/CXL, DDRx/LPDDRx integration verification in SoC RTL.
• Project experience with ARM based ecosystem components (A-series ARM Cores, SMMU, GIC, Core sight, NIC and other complex bus interconnects )
• Your key responsibilities will include writing test plans, defining test methodologies, developing SystemVerilog/Verilog testbenches and tests, and debugging of test failures and issues.
• Working with project management and leads on planning tasks, setting schedules, and quality checkpoints.
• Collaborate with engineers from design & performance analysis

Required Skills and Experience :

• Proven understanding of digital hardware design and Verilog/System Verilog HDL
• Experienced in one or more of various verification methodologies – UVM/OVM, formal, power aware verification, emulation
• Exposure to all stages of verification: requirements collection, creation of verification methodology plans, test plans, testbench implementation, test case development, documentation, and support
• Good Problem Solving and Debugging skills.

“Nice To Have” Skills and Experience :

• Knowledge of SoC Verification Flow and strategy.
• Experience with ARM-based designs and/or ARM System Architectures.

Responsibilities :

• You should be a verification engineer with a knowledge of SoC integration verification, SoC scenario verification, SoC performance verification, CHI/PCIe/CXL, DDRx/LPDDRx integration verification in SoC RTL.
• Project experience with ARM based ecosystem components (A-series ARM Cores, SMMU, GIC, Core sight, NIC and other complex bus interconnects )
• Your key responsibilities will include writing test plans, defining test methodologies, developing SystemVerilog/Verilog testbenches and tests, and debugging of test failures and issues.
• Working with project management and leads on planning tasks, setting schedules, and quality checkpoints.
• Collaborate with engineers from design & performance analysis

Required Skills and Experience :

• Proven understanding of digital hardware design and Verilog/System Verilog HDL
• Experienced in one or more of various verification methodologies – UVM/OVM, formal, power aware verification, emulation
• Exposure to all stages of verification: requirements collection, creation of verification methodology plans, test plans, testbench implementation, test case development, documentation, and support
• Good Problem Solving and Debugging skills.

“Nice To Have” Skills and Experience :

• Knowledge of SoC Verification Flow and strategy.
• Experience with ARM-based designs and/or ARM System Architectures.

Responsibilities :

• You should be a verification engineer with a knowledge of SoC integration verification, SoC scenario verification, SoC performance verification, CHI/PCIe/CXL, DDRx/LPDDRx integration verification in SoC RTL.
• Project experience with ARM based ecosystem components (A-series ARM Cores, SMMU, GIC, Core sight, NIC and other complex bus interconnects )
• Your key responsibilities will include writing test plans, defining test methodologies, developing SystemVerilog/Verilog testbenches and tests, and debugging of test failures and issues.
• Working with project management and leads on planning tasks, setting schedules, and quality checkpoints.
• Collaborate with engineers from design & performance analysis

Required Skills and Experience :

• Proven understanding of digital hardware design and Verilog/System Verilog HDL
• Experienced in one or more of various verification methodologies – UVM/OVM, formal, power aware verification, emulation
• Exposure to all stages of verification: requirements collection, creation of verification methodology plans, test plans, testbench implementation, test case development, documentation, and support
• Good Problem Solving and Debugging skills.

“Nice To Have” Skills and Experience :

• Knowledge of SoC Verification Flow and strategy.
• Experience with ARM-based designs and/or ARM System Architectures.

About the Role

Pattern is building a next-generation AI-driven drug discovery platform that integrates state-of-the-art structural modeling, generative design, and reinforcement learning agents to explore the vast chemical space for novel small-molecule therapeutics.

We are seeking a Ligand Design & Pose Prediction Lead to guide de novo small-molecule exploration, interpret protein–ligand binding predictions, and prioritize compounds for synthesis/testing. You will work in close partnership with a deep learning specialist to combine cutting-edge AI tools with your medicinal chemistry and structure-based design expertise.

This is a strategic, non-lab role — your primary focus will be to bridge AI outputs with biological and chemical insight, ensuring the most promising designs move forward.

Key Responsibilities

Lead ligand pose prediction workflows using state-of-the-art AI and computational docking tools (e.g., DiffDock, EquiBind, Glide, GOLD).

Evaluate protein–ligand binding interactions for fit, contact quality, and structural plausibility.

Collaborate with AI/deep learning engineers to refine de novo molecular generation strategies using models such as REINVENT, Pocket2Mol, and diffusion-based 3D generators.

Apply drug-likeness, ADMET, novelty, and selectivity criteria to prioritize compound candidates.

Integrate binding mode insights with biological context from Pattern’s Agentix Knowledge Graph to align compounds with target mechanism-of-action.

Generate clear, actionable compound selection lists for partner synthesis and in-vitro testing.

Contribute to feedback loops by incorporating experimental assay data into ongoing model optimization.

Present binding hypotheses, SAR rationale, and prioritization strategies to cross-functional teams.

Qualifications Required

PhD or Masters in Medicinal Chemistry, Chemical Biology, Computational Chemistry, or related discipline (or MSc + 3+ years of relevant experience).

Proven experience in structure-based drug design and ligand pose evaluation.

Strong working knowledge of protein–ligand binding principles (H-bonds, hydrophobic contacts, electrostatics, shape complementarity).

Familiarity with AI/ML-based molecular design platforms.

Ability to work with predicted protein structures (AlphaFold/OpenFold) and assess binding pockets.

Experience applying drug-likeness rules and property-based filtering in lead prioritization.

Excellent communication skills and ability to work cross-functionally with AI engineers, biologists, and medicinal chemists.

Preferred

Familiarity with pharmacophore modeling and pocket geometry analysis.

Experience in multi-objective optimization (binding, ADMET, novelty).

Exposure to reinforcement learning-driven compound optimization workflows.

Why Join Us?

You’ll be joining at the frontier of AI-guided drug discovery, working side-by-side with deep learning experts to build a platform capable of efficiently searching through 10^60 chemical possibilities. Your expertise will directly shape the quality and novelty of our candidate compounds, accelerating the path from target to therapy.

About the Role

Pattern is building a next-generation AI-driven drug discovery platform that integrates state-of-the-art structural modeling, generative design, and reinforcement learning agents to explore the vast chemical space for novel small-molecule therapeutics.

We are seeking a Ligand Design & Pose Prediction Lead to guide de novo small-molecule exploration, interpret protein–ligand binding predictions, and prioritize compounds for synthesis/testing. You will work in close partnership with a deep learning specialist to combine cutting-edge AI tools with your medicinal chemistry and structure-based design expertise.

This is a strategic, non-lab role — your primary focus will be to bridge AI outputs with biological and chemical insight, ensuring the most promising designs move forward.

Key Responsibilities

Lead ligand pose prediction workflows using state-of-the-art AI and computational docking tools (e.g., DiffDock, EquiBind, Glide, GOLD).

Evaluate protein–ligand binding interactions for fit, contact quality, and structural plausibility.

Collaborate with AI/deep learning engineers to refine de novo molecular generation strategies using models such as REINVENT, Pocket2Mol, and diffusion-based 3D generators.

Apply drug-likeness, ADMET, novelty, and selectivity criteria to prioritize compound candidates.

Integrate binding mode insights with biological context from Pattern’s Agentix Knowledge Graph to align compounds with target mechanism-of-action.

Generate clear, actionable compound selection lists for partner synthesis and in-vitro testing.

Contribute to feedback loops by incorporating experimental assay data into ongoing model optimization.

Present binding hypotheses, SAR rationale, and prioritization strategies to cross-functional teams.

Qualifications Required

PhD or Masters in Medicinal Chemistry, Chemical Biology, Computational Chemistry, or related discipline (or MSc + 3+ years of relevant experience).

Proven experience in structure-based drug design and ligand pose evaluation.

Strong working knowledge of protein–ligand binding principles (H-bonds, hydrophobic contacts, electrostatics, shape complementarity).

Familiarity with AI/ML-based molecular design platforms.

Ability to work with predicted protein structures (AlphaFold/OpenFold) and assess binding pockets.

Experience applying drug-likeness rules and property-based filtering in lead prioritization.

Excellent communication skills and ability to work cross-functionally with AI engineers, biologists, and medicinal chemists.

Preferred

Familiarity with pharmacophore modeling and pocket geometry analysis.

Experience in multi-objective optimization (binding, ADMET, novelty).

Exposure to reinforcement learning-driven compound optimization workflows.

Why Join Us?

You’ll be joining at the frontier of AI-guided drug discovery, working side-by-side with deep learning experts to build a platform capable of efficiently searching through 10^60 chemical possibilities. Your expertise will directly shape the quality and novelty of our candidate compounds, accelerating the path from target to therapy.

About the Role

Pattern is building a next-generation AI-driven drug discovery platform that integrates state-of-the-art structural modeling, generative design, and reinforcement learning agents to explore the vast chemical space for novel small-molecule therapeutics.

We are seeking a Ligand Design & Pose Prediction Lead to guide de novo small-molecule exploration, interpret protein–ligand binding predictions, and prioritize compounds for synthesis/testing. You will work in close partnership with a deep learning specialist to combine cutting-edge AI tools with your medicinal chemistry and structure-based design expertise.

This is a strategic, non-lab role — your primary focus will be to bridge AI outputs with biological and chemical insight, ensuring the most promising designs move forward.

Key Responsibilities

Lead ligand pose prediction workflows using state-of-the-art AI and computational docking tools (e.g., DiffDock, EquiBind, Glide, GOLD).

Evaluate protein–ligand binding interactions for fit, contact quality, and structural plausibility.

Collaborate with AI/deep learning engineers to refine de novo molecular generation strategies using models such as REINVENT, Pocket2Mol, and diffusion-based 3D generators.

Apply drug-likeness, ADMET, novelty, and selectivity criteria to prioritize compound candidates.

Integrate binding mode insights with biological context from Pattern’s Agentix Knowledge Graph to align compounds with target mechanism-of-action.

Generate clear, actionable compound selection lists for partner synthesis and in-vitro testing.

Contribute to feedback loops by incorporating experimental assay data into ongoing model optimization.

Present binding hypotheses, SAR rationale, and prioritization strategies to cross-functional teams.

Qualifications Required

PhD or Masters in Medicinal Chemistry, Chemical Biology, Computational Chemistry, or related discipline (or MSc + 3+ years of relevant experience).

Proven experience in structure-based drug design and ligand pose evaluation.

Strong working knowledge of protein–ligand binding principles (H-bonds, hydrophobic contacts, electrostatics, shape complementarity).

Familiarity with AI/ML-based molecular design platforms.

Ability to work with predicted protein structures (AlphaFold/OpenFold) and assess binding pockets.

Experience applying drug-likeness rules and property-based filtering in lead prioritization.

Excellent communication skills and ability to work cross-functionally with AI engineers, biologists, and medicinal chemists.

Preferred

Familiarity with pharmacophore modeling and pocket geometry analysis.

Experience in multi-objective optimization (binding, ADMET, novelty).

Exposure to reinforcement learning-driven compound optimization workflows.

Why Join Us?

You’ll be joining at the frontier of AI-guided drug discovery, working side-by-side with deep learning experts to build a platform capable of efficiently searching through 10^60 chemical possibilities. Your expertise will directly shape the quality and novelty of our candidate compounds, accelerating the path from target to therapy.