Software Engineer, AI Product (Canada)
As a Senior Applied AI Engineer at Vanta, you will work cross-functionally to design and implement AI-powered features that deliver customer value and integrate large language models (LLMs) with Vanta's existing products and systems. You will collaborate with product engineers across Vanta to understand how AI systems can accelerate product adoption, instrument evaluations, guardrails, and monitoring, and review customer usage to continually improve quality. Additionally, you will collaborate with AI Platform engineers on foundational AI systems and tooling to accelerate product teams, make pragmatic tradeoffs considering business priorities, user experience, and sustainable technical foundation, mentor engineers, champion good technical and product instincts, and model a collaborative, high-ownership engineering culture.
Engineering Manager, AI
As an Engineering Manager at Vanta, you will build and scale a high-performing team by hiring strategically to fill skill gaps as the team grows. You will coach, mentor, and create an environment that enables your team to do their best work and deliver for the business. You will set direction and guide technical strategy for AI agent and downmarket products, ensuring long-term value aligned with Vanta's business priorities. You will partner closely with product, design, and AI platform teams to ship customer-facing AI features that automate audit work while maintaining human-in-the-loop controls. Additionally, you will champion best practices for applied AI, including prompt engineering, retrieval-augmented generation (RAG), agentic frameworks, and quality evaluation. You will also navigate rapid change and ambiguity with adaptability, iterating quickly on roadmaps as the team's charter and direction evolve.
Software Engineer, ML Data Infrastructure
The Software Engineer, ML Data Infrastructure will collaborate with engineers to build advanced AI design experiences, tackle complex technical challenges including scaling distributed systems and enabling generative media experiences, build robust data infrastructure at petabyte scale ensuring reliability and performance across multi-modal training pipelines, optimize data processing workflows for high throughput involving distributed systems, TPU infrastructure, and large-scale storage, and partner with research scientists to understand data requirements and translate them into production-grade systems to accelerate model development cycles.
Research Engineer / Research Scientist (Pre-training)
In this role, you will push the frontier of visual generative models. You will work on large-scale pre-training for text-to-image foundation models, shaping objectives, algorithms, data, and systems, and turn novel ideas into models that power products used by millions of users. You will work with a creative and ambitious team of researchers and engineers building the future of the creative economy.
Full Stack Product Engineer
As a Full-Stack Product Engineer at Ideogram, you will build products that bring generative AI directly to creators, working across the entire technology stack from designing user experiences to optimizing backend systems that serve millions. Your focus will be on shipping features that users love by combining product intuition, strong ownership, and user empathy. You will design APIs and data models to support evolving product needs, utilize AI-native engineering tools to speed up development, debugging, and understanding of the codebase, and work effectively across frontend and backend systems. You will also be responsible for explaining technical concepts to both technical and non-technical stakeholders, participating in constructive code reviews, collaborating with the team, and taking full responsibility for the outcomes of your work, not just the code.
Optical Engineer - Freelance AI Trainer
Contributors may design original optics problems that simulate real physics research workflows, ensure these problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, base problems on real research challenges or practical applications from optics and physics practice, and document problem statements clearly with verified correct answers.
Junior Software Engineer
Oversee the end-to-end lifecycle of data acquisition and management for foundation models across 3D, video, image, and audio. Identify and acquire diverse datasets from public and commercial partners while managing complex technical and legal requirements. Collaborate with research teams to ensure data sources align with specific model training and fine-tuning needs. Manage technical lifecycle of large-scale data, including ingestion, curation, and AWS S3 storage optimization, ensuring system reliability and code quality. Develop internal tools and standards to make datasets searchable, accessible, and efficiently indexed for research. Partner with legal team to mitigate risks, ensure global regulatory compliance, and manage sensitive data protection rules. Represent the company in legal matters including providing testimony regarding data usage and licensing. Lead data vendor management by negotiating Master Services Agreements and Statements of Work, oversee partnerships for data annotation, evaluation, and collection projects. Drive cross-functional alignment between technical leads and researchers to ensure data strategy supports the company product roadmap.
Materials Engineer & Python Expert - Freelance AI Trainer
Design computational material science problems to challenge a frontier AI model, ensuring each problem has an answer verifiable by code and requires a specialized tool such as ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Pick an anchor tool and design problems based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines. Write Python reference solutions, supply necessary input files and model or domain definitions, decide on numerical answers with domain-appropriate tolerance, and test the problems against the AI model through batches of parallel attempts. Tune problem difficulty to achieve a pass rate in the 10–30% range, rewriting scenarios and adjusting parameters as needed. Submit problems for senior review to ensure quality standards. Gain deep understanding of anchor tools and AI model behavior through the iterative calibration and testing process.
Intermediate Full Stack Software Engineer
The role involves implementing full stack features end-to-end across front-end, back-end, and cloud infrastructure layers, building and integrating RESTful APIs and cloud-hosted services primarily on Azure, developing front-end components using modern JavaScript/TypeScript frameworks, writing unit, integration, and API tests, using Docker for local development and containerized deployments, and managing work in Git with effective collaboration with AI agents. The engineer will build features that incorporate large language model (LLM) calls via the Claude API or Azure OpenAI, implement retrieval-augmented generation (RAG) components and tool integrations, write evaluation harnesses for LLM-powered features, and document LLM feature behavior clearly. They will participate actively in technical design discussions, use Claude and AI-assisted development tools for prototyping, code generation, and debugging, write clear prompts and specs for AI agents, review AI-generated code critically, and contribute to the development of AI-assisted workflows. Collaboration includes participating in code reviews, working closely with ML engineers, data engineers, and product managers, contributing reusable components to shared libraries, engaging in sprint ceremonies, and proactively seeking feedback to grow technically and toward leadership.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Design computational engineering problems to challenge a frontier AI model, ensuring the problem has an answer verifiable by code and requires a specialized tool like Cantera, CoolProp, CalculiX, OpenFAST, or others. Each problem runs inside a sealed Linux container with the pre-installed tool and a programmatic judge that grades the model's answer. Tasks include picking an anchor tool and designing a problem that hinges on its solvers, simulation kernels, or domain-specific models, writing a Python reference solution, supplying input files and geometry or mechanism definitions as needed, deciding the numerical answer and tolerance for correctness, testing the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts, and submitting tasks to a senior reviewer for feedback and quality assurance. Calibration involves tuning the problem against batches of parallel runs to aim for a pass rate in the 10–30% band, rewriting thermodynamic cycles, tightening material models and boundary conditions, and observing how the agents behave.
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