AI Engineer

• Co-create solutions with business and technology stakeholders through workshops, rapid iterations, and hands-on delivery
• Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows
• Prototype, test, deploy, monitor, and improve solutions in real customer environments

Forward Deployed AI Engineer

• Work on adding multilingual features to chatbots
• Conduct benchmarking to enhance language detection in chatbots
• Participate in testing and quality assurance of AI systems
• Implement changes to improve chatbot efficiency and prevent premature transfers to agents
• Coordinate with support and customer experience teams for feature requests
• Perform ongoing chat analysis for effectiveness assessment
• Design AI-powered chatbots for customer interaction in various fields
• Automate processes using AI to improve efficiency
• Develop scalable AI infrastructures and integrate new machine learning techniques
• Evaluate and work with various AI models for different applications
• Contribute to the roadmap for integrating speech-to-text and real-time voice APIs

AI Engineering – Voice API

• Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients
• Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration
• Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions
• Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing
• Help shape internal best practices, tooling, and technical standards as the team grows
• Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences

Mid/Senior AI Engineer

• Design, build, and scale enterprise AI, ML, and data products across cloud environments
• Architect and deliver end-to-end AI/ML and generative AI solutions
• Design secure application programming interfaces (APIs), integration services, data pipelines, and orchestration workflows
• Serve as the AI/ML technical authority for cross-functional teams
• Lead architecture reviews, AI readiness assessments, performance benchmarking
• Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, and cost optimization
• Mentor engineers and data scientists
• Support solutioning activities by shaping technical approaches

AI/ML Delivery Engineer

• Identify and shape opportunities for AI-enabled improvement
• Redesign work where needed to enhance workflow
• Support delivery in a structured manner with clear outcomes
• Connect business, technical and governance partners for practical improvements
• Measure value and capture learning from AI initiatives
• Support adoption of new ways of working with teams

AI Workflow Transformation Lead

• Ship full-stack AI projects end-to-end on a product built in Ruby on Rails backend and React frontend
• Design and run experiments to validate AI features
• Build and integrate components for AI infrastructure
• Improve processes, tools, and systems to scale AI features
• Help stay up-to-date with cutting-edge AI research
• Work closely with Circle's leaders and designers
• Foster a bias for action by prioritizing speed of iteration

Senior Full-stack Engineer, Applied AI

Machine Learning Engineer

• Own the full ML lifecycle from data collection to ongoing quality assessment.
• Train CV/ML models to detect deepfakes and uncover complex anomaly patterns.
• Balance research and speed in model building and heuristic solutions.
• Serve models efficiently with FastAPI and PyTorch, integrating with Java backend.
• Build and monitor ML pipelines and dashboards with tools like Airflow and ClickHouse.

Senior Machine Learning Engineer

AI Research Scientist

• Scoping vague but important questions around AI x Animals — choosing the right depth, angle, and methodology for each, from literature reviews to modelling, data analysis, or expert interviews.
• Conducting independent research on complex questions, addressing uncertainty and competing perspectives explicitly.
• Engaging external stakeholders to understand their needs and shape deliverables to their specifications.
• Producing decision-useful outputs from inception to delivery, in the best-fit format (report, model, dashboard), translated into practical recommendations for decision-makers.
• Managing projects end to end — keeping them on track, reporting progress, and proactively identifying mitigations when barriers arise.
• The balance of these responsibilities, and the autonomy you'll have, depends on the level you're hired at. At the Senior Researcher level, you will own these responsibilities at higher autonomy and help co-create the overall research strategy for the program. You will also build and steward more external relationships with funders, advocates, and field experts.

Senior Researcher – AI, Animals

• Own organic growth: Drive SEO and AEO/AIO performance across traffic, signups, and share of voice in AI answers you set the targets and you hit them.
• Set and run the roadmap: From keyword and prompt research to publishing priorities, programmatic systems, and link strategy you decide what we ship and in what order.
• Reposition Softr in LLMs: Move us off "no-code portal builder" and into accurate, current positioning across ChatGPT, Perplexity, Gemini, and Claude. Track it in Profound, close citation gaps, and engineer the content patterns LLMs actually quote.
• Lead technical and programmatic SEO: Site health, indexation, Core Web Vitals, schema, and internal linking plus scoping programmatic page systems with engineering and managing quality at scale.
• Build link, citation, and Reddit authority: Drive link and citation building across owned, earned, and partner channels, with a deliberate Reddit strategy that earns mentions LLMs and Google both trust.
• Partner, report, and pitch new bets: Brief Content on structure and on-page, report monthly on SEO/AEO performance, flag risks early, and translate algorithm and LLM behavior changes into actions for the team.

SEO, AI Search Lead

NLP Engineer

• Train and fine-tune language models powering our AI companions
• Own and improve agent harnesses, agentic loops, and the chat interface algorithm
• Build and maintain the full LLM stack — from model training to production deployment
• Track cutting-edge NLP research and open-source developments; translate them into an NLP roadmap
• Collaborate closely with the validation, content, and dataset preparation teams to design experiments and measure model quality

Senior NLP Engineer

• Rethink the PM job when most users are agents.
• Talk to customers across the full spectrum.
• Own the problem statement and requirements behind every agent tooling bet.
• Decide what gets built, what gets deferred, and what gets cut.
• Define how each launch is measured before it ships.
• Keep engineering, design, and leadership aligned.

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