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Senior Silicon ML Engineer
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Lead system architecture and integration with internal silicon design tools and workflows in LLM-based agentic systems
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Cognichip

Senior Silicon ML Engineer

Toronto, ON, Canada

$150,000 to $180,000 CAD / year · Full-time

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Job details

Pay: $150,000 to $180,000 CAD / year
Job type: Full-time
Apply by: Dec 3, 2026
Posting language: English
Posted: Nov 3, 2026

Original job description

Job Title

Senior Silicon ML Engineer

Senior Silicon ML Engineer

At Cognichip, we’re not just building AI - we’re redefining what’s possible at the nexus of silicon design and machine learning. As a Silicon ML Engineer on our team, you’ll fuse knowledge of chip design and ML into end-to-end silicon design flows. If you thrive on pushing the envelope of domain-specific research and translating cutting-edge ideas into production-grade systems, this is your stage.

Core Responsibilities

Design and implement AI-enabled silicon design and verification workflows with explicit incorporation of hardware domain knowledge and constraints
Lead system architecture and integration with internal silicon design tools and workflows in LLM-based agentic systems
Own end-to-end the curation, cleaning, and structuring of silicon design and verification datasets or ML/LLM/agentic system with domain-aware labeling and quality controls
Encode domain structure, rules, and expert feedback into ML/DL model pipelines, prompts, evaluation, and system logic
Collaborate directly with other chip design and verification engineers to ensure domain correctness and practical usability
Build AI for hardware research prototypes and convert them into scalable, production-quality systems integrated with design flows
Produce technical documentation and communicate design decisions and tradeoffs to cross-functional stakeholders

Required Qualifications

BS in Computer Science, Electrical Engineering, or a related field plus 4+ year of full- time industry experience OR MS in Computer Science, Electrical Engineering, or a related field plus 3+ year of full-time industry experience OR PhD in a relevant field with 2+ year of applied research or industry experience
Strong programming skills in Python (PyTorch or similar ML frameworks preferred)
Demonstrated ability to turn research prototypes to production systems
Experience working with messy, real-world data and defining quality metrics
Demonstrated exposure to hardware design, verification, or EDA workflows through coursework, research, or industry work
Proven track record of collaboration with with other deeply technical teams

Preferred Qualifications

Background or exposure to silicon design / EDA workflows (e.g., RTL, verification, specs, UVM, synthesis) and hardware description language, e.g., verilog or systemverilog
Industry experience with LLMs, agentic systems, or domain-adapted AI systems
Experience with synthetic or programmatic data generation, simulation-driven data, or otherwise automated dataset construction
Experience using Databricks or similar large-scale data processing platforms
Experience collaborating in fast-moving, cross-functional teams

What We Offer

Work on foundational and unsolved problems at the frontier of AI and hardware
Real ownership in your work that directly shapes core product capabilities.
Work with a team of high-caliber collaborators at the intersection of AI, cloud, and semiconductor design.
A culture of innovation, precision, and impact, where your work directly shapes the future of engineering.
Competitive compensation package, including equity participation.

Who This Role Is Great For

Engineers excited to work across ML, systems, and silicon
Mid-career ML engineers with strong fundamentals and real-world experience
Early-career PhD graduates who want to see research ideas actually ship
People who like ambiguity and turning vague problems into concrete systems

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More about this job

Responsibilities

Design and integrate AI-enabled silicon design and verification workflows, incorporating hardware domain knowledge into agentic systems, datasets, model pipelines, and evaluation. Collaborate with chip design and verification engineers to turn hardware AI research prototypes into scalable production systems and document technical decisions.

Requirements

Requires a relevant bachelor's degree plus at least four years of industry experience, a master's degree plus at least three years, or a PhD plus at least two years of applied research or industry experience. Candidates need strong Python skills, experience moving research prototypes into production, real-world data quality experience, exposure to hardware design or EDA workflows, and a record of technical collaboration.

Skills

Python
PyTorch
Machine Learning
Deep Learning
Large Language Models
Agentic Systems
Silicon Design
Hardware Design
Design Verification
Electronic Design Automation
RTL
UVM
Verilog
SystemVerilog
Data Curation
Production Systems

Benefits

Equity Participation

Visa sponsorship

Not detected in the job text

Keywords

Python
PyTorch
Machine Learning
Deep Learning
Large Language Models
Agentic Systems
Silicon Design
Hardware Design
Design Verification
Electronic Design Automation
RTL
UVM
Synthesis
Verilog
SystemVerilog
Data Curation
Data Cleaning
Domain-Aware Labeling
Quality Metrics
Synthetic Data Generation
Simulation-Driven Data
Databricks
AI-Enabled Workflows
Production Systems
System Architecture
Hardware Domain Knowledge

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