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
- Posting language
- English
- Posted
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
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
- 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
- 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
- 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.
- 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
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
Categories
- Engineering
- Technology
- Software
- Data & Analytics
- Science & Research
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