Senior Silicon ML Engineer

Title: Senior Silicon ML Engineer

Location: Toronto, ON, Canada

**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