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.
Build and improve AI agents by writing production code for Voice Agent and Autopilot platforms.
Own enterprise AI deployments from initial discovery through go-live, working directly with customers.
Design and implement AI workflows, debug agents in production, and turn customer needs into reusable product features.
Supermove builds software and AI for moving companies, providing systems for sales, operations, crews, and billing. The company has raised $30M from investors like Andreessen Horowitz and serves moving businesses across the US, Canada, and Australia.
Set the technical direction for AI engineering across the team: agent architecture patterns, evaluation methodology, deployment and monitoring strategies
Design the AI platform layer, including shared agent frameworks, tool integrations, and evaluation infrastructure
Work directly with clients on the most complex engagements, identifying new problem domains and ensuring production quality
Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. With over 1,500 firms in 60 countries managing nearly $10 trillion in assets, Addepar fosters a culture of ownership, collaboration, and innovation.
Build evaluations to measure and improve AI reasoning in complex, high-stakes decisions.
Collaborate with engineers, product, and domain experts to turn observed failures into testable improvements.
Own the quality of shipped systems, from code reviews to monitoring and integration.
Elicit builds AI systems that help research and business teams make evidence-based decisions on high-stakes questions. They are a remote-friendly, collaborative team that values rigorous thinking, experimentation, and in-person retreats.
Architect AI infrastructure including backend services, data pipelines, and orchestration workflows for LLMs.
Write production-grade, high-performance code for high-throughput AI workflows and data-driven systems.
Integrate foundational models, vector data stores, and cloud microservices into scalable platform components.
Korza builds AI infrastructure and products, specializing in LLM and agent-based systems. The company emphasizes cross-functional collaboration and rapid shipping of cutting-edge AI capabilities.
Lead the engineering strategy and execution for evaluations of AI agents, owning the core evaluation platform.
Design scalable evaluation infrastructure, APIs, workflows, and production systems across software categories.
Mentor and develop a team of engineers as the technical authority on agentic evaluation.
This company focuses on building credible, scalable evaluations of AI agents from software vendors. It operates as a fully remote, inclusive team with a flexible culture and a focus on professional growth.
Own agent quality end-to-end for Bliro's chat and voice agents, including prompting, context, and model evaluation.
Build eval sets, LLM judges, and benchmarks to measure quality, latency, and cost with statistical rigor.
Establish a repeatable process for improving agent quality and set the long-term technology roadmap.
Bliro builds an AI assistant that handles desk work for field sales teams, with over one million customer touchpoints documented. Backed by leading investors and trusted by German Mittelstand companies, it's a small, high-performing team that values ownership and continuous improvement.
Build AI-powered agents and tools end-to-end: identify problems with clients, prototype quickly, evaluate rigorously, and ship to production.
Design and implement LLM integrations including prompt engineering, function calling, structured outputs, and retrieval-augmented workflows.
Work directly with clients to understand workflows, validate AI behavior, and iterate toward production quality.
Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. With over 1,500 firms in 60 countries and nearly $10 trillion in assets on the platform, Addepar fosters a culture of ownership, collaboration, and continuous learning.
Build production AI and agentic workflows end to end, from PRD and architecture through implementation, evaluation, and iteration.
Own the retrieval stack, model selection, and evaluation harness to ensure measurable quality, latency, and cost.
Design and maintain a reusable component library and set engineering standards for the CoE and partner teams.
QAD | Redzone builds an intelligent manufacturing and supply chain platform that connects people, processes, and data into a single System of Action. It fosters a collaborative culture of smart, hard-working people who support one another and prioritize diversity, equity, and inclusion.
Develop and deploy cutting-edge AI/ML solutions to enhance the platform and improve student learning experiences.
Design, develop, and optimize LLM-powered agentic systems and APIs for real product use cases.
Collaborate with senior engineers and contribute to evaluation frameworks and MLOps pipelines.
Interview Kickstart specializes in interview preparation and career transitions into high-demand tech fields like AI, ML, and Data Science. Over 17,000 tech professionals have been guided by current and former hiring managers to land coveted positions at companies like Google and Amazon.
Design AI evaluations: define ground truth, metrics, and scoring methods for models and agents.
Build repeatable eval loops to track quality over time and catch regressions before release.
Translate eval results into actionable recommendations for system improvements.
Alpaca is a global leader in agent-first brokerage infrastructure, providing APIs for stocks, ETFs, options, crypto, and more. We're a diverse team of 400+ remote-first members backed by $400M in funding, committed to opening financial services to everyone.
Build AI agents that take action: tools, prompts, guardrails, and multi-turn workflows with memory.
Ship the full experience: in-product chat, streaming responses, permission-aware APIs, and MCP server.
Make quality measurable: build evals, catch regressions, and use production feedback to improve quality, latency, and cost.
Muck Rack builds an AI-powered PR platform that helps organizations understand their brand across news, social media, and AI answers. It is a founder-controlled, fully distributed firm trusted by 3,000+ organizations, with an inclusive, customer-first culture.
Design and build automated evaluation pipelines for AI agents.
Develop testing frameworks for reasoning, multi-turn conversations, and adversarial scenarios.
Own the evaluation architecture and integrate observability tools in a fully remote team.
A company building production-grade AI agents and systems. They operate in a fully remote, globally distributed environment with a culture focused on innovation, autonomy, and continuous improvement.
Design, build and operate production AI agents, including an autonomous coding agent that works from Jira and Slack, opening PRs and fixing CI failures.
Build the retrieval and context layer behind our AI brain: embeddings, semantic search, knowledge-graph modeling, and integration with Snowflake and internal docs.
Harden agents against prompt injection and over-broad permissions, and drive cost and latency down using caching and structured outputs.
Weedmaps is a global leader in the cannabis industry, providing a platform for consumers and businesses. The company has a collaborative culture focused on innovation and community, serving the U.S. and worldwide.
Own AI workflows end to end, from problem definition to production and improvement, for finance processes like invoice-to-payment and reconciliation.
Design and build production-grade LLM-based systems with structured outputs, RAG, validation, and monitoring.
Collaborate with product, backend engineering, and customers while setting technical direction and using AI coding tools daily.
Jeeves is a stablecoin-native banking platform providing corporate cards, payments, treasury, and spend management for global enterprises. With over $5B in annualized volume and backing from top investors, the company is scaling rapidly and values relentlessness and tackling hard problems.
Own AI agents and autonomous workflows powered by LLMs from problem framing through production.
Build RAG pipelines, set evaluation and guardrail standards, and optimize for cost and scalability.
Work directly with client teams to scope problems and deploy AI systems in their environments.
Acceler is a Forward-Deployed Engineer company that takes AI from pilot to production inside client systems. With a practitioner network of nearly 1,000 engineers across the US and India, it's a high-caliber team built on ownership, trust, and rapid learning.
Design and build production-grade AI agents with LangGraph to automate multi-step workflows across internal teams.
Deploy and scale agents on Amazon Bedrock AgentCore, ensuring security, reliability, and cost efficiency.
Partner with internal teams to identify high-impact workflows and drive adoption through documentation and training.
Wrapbook is an AI platform for production finance, helping finance teams manage payroll, spend, and accounting for film, TV, and commercial productions. Trusted by teams at Netflix and Paramount, the 350+ person company is backed by major investors and focused on using AI to transform how finance teams work.
Design agent architectures for planning, reasoning, tool use, and memory integration.
Improve reliability on long-running tasks with failure recovery and evaluation systems.
Build loops for agents to improve with real use, balancing quality, latency, and cost.
Adaption builds AI systems that evolve in real-time, making them flexible and personalized. They are a global-first team focused on talent density and collaboration.