Design and ship features end to end across data model, API, background workers, and UI.
Build and improve agent and model evaluation: sandboxed trials, scoring, and trustworthy aggregation of thousands of runs.
Improve benchmark task quality checks, integrate new models and agents, and keep the system fast and reliable as usage grows.
Turing accelerates superintelligence by providing high-quality datasets, reinforcement learning environments, and research benchmarks to frontier AI labs, and also deploys agentic AI systems for Fortune 500 enterprises. Headquartered in San Francisco, it operates with startup speed and an AI-forward culture.
Architect and implement production-ready generative AI and LLM-based solutions within the product suite
Design and develop agentic systems, RAG pipelines, and evaluation workflows to automate tasks and improve reliability
Collaborate with product, design, and engineering teams to turn complex AI concepts into user-facing features
JUPUS is a fast-growing legal tech company in Europe, building the JUPUS platform to improve productivity for law firms. The culture is flat, fully remote, and fast-moving, with direct access to engineering leadership and a focus on practical AI systems.
Lead and execute the AI roadmap across voice, chat, and document products.
Design and build production-ready generative AI systems, including agentic workflows and RAG.
Mentor and grow the AI team while establishing evaluation and observability best practices.
JUPUS is a legal tech company building AI products for law firms, including a website chatbot, an AI phone assistant, and document analysis grounded in German court rulings. It is one of the fastest-growing legal tech companies in Europe with a flat hierarchy and an execution-focused culture.
Build a pipeline for parsing clinical documents and validating executable modules.
Design and refine a target syntax for representing guideline logic.
Run experiments across open and closed models, fine-tuning approaches, and constrained decoding.
They build a compiler that turns clinical guidelines into structured, executable decision logic for healthcare. The team is research-focused with production engineering stakes, working closely with clinicians and technical leadership.
Build pipelines that parse clinical standards and transform them into executable modules using document and layout models.
Evaluate open and closed models, fine-tuning approaches, and constrained decoding strategies.
Develop datasets, benchmarks, metrics, and adversarial cases to assess clinical fidelity and correctness.
They build a clinical AI compiler that turns healthcare standards into validated, executable decision logic. The team is research-focused, collaborative, and works closely with clinicians to make recommendations traceable and reliable.
Build tools for understanding traces, evaluating agent behavior, and turning results into improvements.
Run experiments on Mastra's primitives to find where they fall short and improve configuration and behavior.
Help turn findings into capabilities developers can use in their own applications.
Mastra is the open-source TypeScript framework for building AI agents. We're a small, fully remote team backed by Y Combinator and Spark Capital with $35M raised.
Build LLM-based agents on the platform's scaffolding, integrating tool calls, internal APIs, and guardrails.
Take agents to production on AWS with containers, CI/CD, secrets, permissions, and security controls.
Define and run evals, monitor with Langfuse, and document runbooks for independent operation.
Muttdata builds innovative Data Products and Machine Learning solutions to help companies solve complex business challenges. It is a fast-growing, remote-first startup that values collaboration, continuous learning, and a positive, ownership-driven culture.