Deployed Engineer (Toronto)
The Deployed Engineer will co-architect and co-build production AI agents with customer engineering teams, own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations, help customers deploy and operate agent-based applications including conversational agents, research agents, and multi-step workflows, and advise customers post-sale on architecture, best practices, and roadmap-level decisions. They will also run technical demos, trainings, and workshops for developer audiences, surface field feedback, contribute reusable patterns, cookbooks, and example code that scale across customers, and occasionally contribute code upstream when it meaningfully improves customer outcomes.
Software Engineering Manager, Autonomous
As an Engineering Manager on the Autonomous team, you will lead and scale a high-calibre team of engineers dedicated to defining the future of AI agent development and advancing AI and backend systems. You will oversee the technical roadmap for the team by translating architectural complexity into clear product strategies, mentor and support the professional growth of a diverse group of engineers, and partner closely with Product and Design to ensure the agent-building tools remain intuitive and technically robust. You will champion a "show > tell" culture to ensure rapid shipping while maintaining high technical stability and user experience standards, and clear technical and operational roadblocks to enable the team to operate with high agency and clarity. You will act as the bridge between product vision and technical execution.
Senior Product Designer, Mobile
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts including development, testing, deployment, and monitoring. Collaborate with engineering teams to ensure they have the tools and visibility needed to build high-quality AI features.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Contributors may design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.
Energy Engineering & Python Expert - Freelance AI Trainer
Contributors may design rigorous energy engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.
Senior AI Engineer - Canada
Senior AI Engineers at Inworld are responsible for researching, building, optimizing, and deploying production machine learning systems that support thousands of developers. Their work focuses on overcoming research and engineering challenges related to speech modeling, including speech-to-text and text-to-speech systems, addressing complex problems such as data collection, training infrastructure, reinforcement learning alignment environments, and ultra-low latency inference optimizations for AI-driven software.
Strategist, Agent Development
As a member of the Agent Strategist function at Sierra, you will partner with Agent Product Managers and Agent Engineers to scope, build, and ship AI agents handling thousands of customer conversations daily. You will play a central role in agent development by combining product strategy, conversational design, and customer insight to create high-quality agents. Responsibilities include being a trusted advisor to customers and driving their AI strategies, building, designing, and refining conversational AI agents, driving execution and delivery of multiple complex and high-visibility agent development projects, coordinating across technical and non-technical stakeholders throughout the agent development lifecycle, ensuring clear communication across all stakeholders and developing strong relationships, and contributing data-driven, strategic insights to customers and internal team decisions.
Software Engineer, Agent
Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth. Own and manage the Agent Development Life Cycle (ADLC) with complete autonomy from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments. Partner with large enterprises and startups to understand business challenges and build AI agents that transform their operations at scale. Build the future of Sierra's core platform by surfacing unmet customer needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's product.
Physics Researcher (Python) - Freelance AI Trainer
Design rigorous physics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.
Software Data Engineer
The Software Data Engineer will collaborate with Machine Learning, Full-stack engineers, and Science teams to address complex document mining challenges and enhance the capture and modeling of scientific experiments. The role involves scaling data pipelines to transition data quickly and reliably from research to the platform, working with both semi-structured and unstructured data sources. Responsibilities include defining and applying best practices for a broad range of cloud-based technologies, architecting and maintaining robust data pipelines that ingest diverse data sources and use large language models (LLMs) for high-fidelity entity extraction into structured formats. The engineer will implement evaluation frameworks to monitor accuracy, drift, and hallucination rates of extraction models within production pipelines. They will lead or consult on engineering design proposals aligned with the unified Platform Stream roadmap, make independent technical decisions based on the business context, proactively identify and implement project improvements, respond urgently to operational issues, own issue resolution within their responsibility scope, and challenge current practices by proposing new technologies or working methods.
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