Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.
About Goaly
About the Role
Key Responsibilities
Requirements
Bonus Points
At Goaly, our mission is to make custom AI affordable for every business. Our founding team comes from the front lines of top AI labs and tech giants (Meta MSL, TikTok AI, Google DeepMind, xAI, Microsoft Research, etc.), where we built large-scale training infrastructure powering trillion-parameter models and scaled GenAI models to a global user base. Now, we are building something we wish we had before: a platform that makes training and adapting custom AI affordable for all modern companies, not just Big Tech. Our north star is ambitious: for a domain-specific task, reach 90% of SOTA performance at less than 10% of the cost. To get a taste of what we are doing, see our first tech blog.
You will sit at the intersection of systems engineering and applied ML, building specialized infrastructure that keeps large language and multimodal models fast, reliable, and cost-effective. You will partner with research, product, and infra teams to ship production-ready platforms for training and serving AI at scale.
- Efficiency & performance: Improve LLM training and inference efficiency through better memory utilization, optimized parallelism, and kernel-level innovations (e.g. FlashAttention, CUDA/Triton).
- Training & RL robustness: Build scalable, stable training and RL pipelines with strong reproducibility, observability, and debuggability.
- Serving & inference optimization: Design and tune high-throughput, low-latency model serving systems, including quantization, caching, and speculative decoding.
- Scalability & infrastructure: Own end-to-end training and inference infrastructure — from data ingestion and checkpointing to multi-GPU and multi-cloud orchestration.
- Production enablement: Work closely with researchers and product engineers to turn new algorithms into reliable, production-ready systems.
- 5+ years building or operating ML infrastructure at scale, ideally supporting large language or multimodal models.
- Deep understanding of GPU architecture, distributed training frameworks (PyTorch, DeepSpeed, Megatron, Ray), and parallelism strategies.
- Hands-on experience running inference stacks (vLLM / SGLang, TGI, Triton) and optimizing them via low-level profiling.
- Strong software engineering fundamentals in Python and one of C++/Rust/Go, with clean, reliable code shipped to production.
- Working knowledge of modern data pipelines, feature stores, and vector databases used in production AI systems.
- Comfort automating infrastructure with Kubernetes, Terraform/Pulumi, and observability stacks (Prometheus, Grafana, OpenTelemetry).
- Experience deploying open-source LLMs (Llama 3, Qwen, DeepSeek) or training custom foundation models.
- Contributions to ML systems tooling (compilers, kernels, inference runtimes) or open-source infrastructure projects.
- Background in reinforcement learning, evaluation harnesses, or alignment tooling that hardens production AI systems.
Similar Jobs
.png)
Cloud • Insurance • Payments • Software • Business Intelligence • App development • Big Data Analytics
The Enterprise Account Executive will focus on acquiring new business in the insurance sector, managing client relationships, and collaborating with sales teams to enhance reach.
Top Skills:
CRMSalesforce
Cloud • Computer Vision • Information Technology • Sales • Security • Cybersecurity
Lead and execute incident response engagements, perform host and network forensics across Windows, macOS, and Linux, conduct basic malware analysis and reverse engineering, develop hunting methods, produce reports and remediation plans, engage with legal and executives, and contribute thought leadership and public-facing content.
Top Skills:
AIAWSAzureBroGCPLinuxmacOSSuricataWindowsZeek
Artificial Intelligence • Fintech • Hardware • Information Technology • Sales • Software • Transportation
Lead Motive's strategy and execution to enter and scale Mexico, the UK, and Canada and evaluate further international expansion. Own go-to-market roadmaps, localization, demand generation, partnerships, and budget allocation. Build and manage a small regional team and agency partners, align cross-functional stakeholders, define success metrics and reporting, run test-and-learn experiments, and present commercial outcomes to executive leadership.
What you need to know about the Toronto Tech Scene
Although home to some of the biggest names in tech, including Google, Microsoft and Amazon, Toronto has established itself as one of the largest startup ecosystems in the world. And with over 2,000 startups — more than 30 percent of the country's total startups — Toronto continues to attract new businesses. Be it helping entrepreneurs manage their finances, simplifying business operations by automating payroll or assisting pharmaceutical companies in launching new drugs, the city's tech scene is just getting started.
