4 days ago
Base Salary
$190k - $286k/yr
Responsibilities
- Identify and test GenAI and agentic ideas for internal productivity and external product use cases.
- Build prototypes, validate them against defined success metrics, and hand off production-ready designs and reference implementations.
- Track emerging models, techniques, and tools and document their applicability to Equinix.
- Benchmark open-weight and commercial models for quality, latency, cost, and license terms.
- Fine-tune, distill, and quantize models for defined tasks, including tool-use and agentic behavior.
- Build per-agent and per-workflow cost models covering tokens, calls, caching, compute, and cost per completed task.
- Test model routing, context compression, caching, batching, and budget-limit cost controls.
- Optimize model serving with the platform team using tools such as vLLM and SGLang.
- Create reproducible experiments with versioned data, code, configurations, and results.
- Use the Agent Intelligence evaluation platform and turn failures or feedback into tuning data, regression tests, or design changes.
- Document intended use, data provenance, limitations, evaluation results, licensing, data boundaries, and security requirements.
- Explain results and tradeoffs to technical and non-technical stakeholders and advise teams on suitable AI approaches.
Requirements
- 5–8 years of experience in ML or software engineering, including 3+ years in LLM or deep-learning development.
- Track record of taking AI ideas from prototype to production or to a documented decision to stop.
- Hands-on experience with fine-tuning or parameter-efficient tuning, distillation, quantization, agent systems, or retrieval systems.
- Experience building evaluation harnesses and using results to make decisions.
- Ability to build cost and performance models for AI workloads.
- Proficiency in Python, PyTorch, and Hugging Face tooling.
- Clear written and verbal communication with non-technical stakeholders.
- Preferred experience with inference and serving, batching, GPU optimization, preference optimization or reinforcement learning, tool-use or function-calling fine-tuning, multi-agent systems, model routing, MLOps, open-weight model licensing, model governance, or responsible-AI programs.
- Published research, patents, or open-source contributions are preferred.
- BS or MS in computer science, machine learning, or a quantitative field, or equivalent experience.
Benefits
- Targeted base pay for the listed locations is CAD 142,000–212,000 annually in Toronto or USD 190,000–286,000 annually in Redwood City; bonus eligibility and possible equity are separate.
- Employee Assistance Program is available to all employees.
- US employees may enroll in health, life, disability, and voluntary insurance plans, retirement plans, accrued PTO, and paid holidays.
- Canada employees may enroll in healthcare, life, disability, and optional benefits; DCPP, RRSP, and TFSA savings plans; vacation; personal time; and paid holidays.
- Benefits have eligibility requirements and may change under applicable plan or program terms.
Categories
About Equinix
Equinix is a public digital infrastructure company that operates a global platform of colocation and interconnection data centers for enterprises, cloud and network providers. It sells space, power, and connectivity, plus services such as Equinix Fabric, Network Edge, and Equinix Metal for hybrid multicloud and edge deployments. Founded in 1998 and headquartered in Redwood City, California, it is listed on NASDAQ (EQIX) and runs hundreds of IBX facilities across major metros worldwide.