Design and maintain scalable data pipelines for AI workloads and intelligent products.
Develop automated workflows for synthetic data generation and large-scale data preparation.
Collaborate with product and engineering teams to optimize data storage, retrieval, and validation.
The company partners with Jobgether to hire for AI-focused data engineering roles. It operates in a remote, project-based environment with a focus on building scalable infrastructure for next-generation AI applications.
Identify and vet consequential opportunities across Data products, tools, AI measurement, and evaluation.
Develop and validate methods for evaluating generative AI and agentic workflows, building prototypes and tools.
Provide technical direction, mentor teams, and communicate findings to C-level executives.
Figma is on a mission to make design accessible to all, building collaborative tools for digital product development. The company is growing its team of passionate creatives and builders, fostering a culture that values diversity and equal opportunity.
Learn, maintain, and improve our synthetic data pipeline and automated training system.
Take over a model and improve it with an agent loop that generates its own data.
Build closed loops where agents train detectors, test against validation sets, and improve with little human input.
CVEDIA builds computer vision models and applications that run on server and edge hardware using synthetic data. We are a small, fully remote team that uses AI agents across the company.
Design and build challenging evaluations and executable environments for coding and long-horizon agents.
Develop scoring logic and verifiers to detect reward hacks and trace failures through model outputs and harness code.
Build and scale RL data pipelines for generating and curating challenging coding and agentic tasks.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models and open-source models trained for long-horizon tasks. The company has raised $150M from top investors and is a mission-driven team passionate about accelerating science and AI.
Own applied RL programs from ambiguous customer problem to deployed improvement loop.
Understand workflows, build environments and evaluations, run post-training experiments, and deploy results.
Work directly with customer researchers, engineers, and leaders, accountable for research result and practical impact.
Prime Intellect builds the open superintelligence stack, providing a full-stack platform for frontier-scale post-training of AI models. The company has raised $150M from top investors and is seeking builders who want to work at the intersection of frontier research, real infrastructure, and go-to-market.
Design pretraining, SFT, and RL data for engineering tasks across CAD, CAE, and EDA.
Define evaluation criteria and agent architectures for multi-step engineering workflows.
Build benchmarks and diagnostics to identify model failures and trace back to gaps in data or agent design.
Mistral is a full-stack AI company building frontier models, developer tools, and compute for enterprises. We are a diverse, dynamic team distributed globally, passionate about AI and committed to low-ego collaboration and innovation.
Train ML models on blockchain data and take them from research to production.
Build AI agents and fine-tune LLMs to generate intelligence beyond hand-built heuristics.
Ship production-quality code with rigorous evaluation and scalable architecture.
TRM Labs provides AI-powered intelligence solutions that help public and private sector agencies investigate and disrupt crime. The team is mission-driven and fast-paced, with experts in law enforcement, data science, engineering, and financial intelligence tackling complex global challenges.
Build labeled evaluations from real agent activity and publish monitor precision and recall.
Run monitors across customer fleets and identify unread events.
Turn patterns into proposed policies and refinements for security engineers.
Sondera develops a policy engine for AI agents that enforces natural-language rules to ensure agents act as intended. The team includes veterans from top security and AI companies and is backed by leading venture firms, emphasizing a remote, writing-intensive culture.
Conduct research to develop new functionalities and improve existing technologies across fraud, voice/speech, video, authentication, and deepfake detection.
Design, implement, and evaluate deep learning algorithms and ML models tailored to complex datasets.
Partner with research and engineering teams to transition prototypes into production-ready systems and publish findings in top conferences.
Pindrop is a cybersecurity company delivering continuous identity verification across voice, video, and digital interactions. Backed by leading investors and protecting billions of interactions annually, we maintain a remote-first culture and focus on innovative security research.
Evaluate GPU kernel tasks for technical accuracy, realism, and reproducibility.
Identify memory allocation bugs, execution bottlenecks, and errors in parallel computing logic.
Provide clear, actionable feedback to improve AI training and evaluation workflows.
A partner company operating a specialized AI training project to improve the technical accuracy of AI systems. The company seeks experienced technical professionals for freelance, remote evaluation work, emphasizing precision and evidence-based assessment.
Build data pipelines that pull information from ERP, CRM, and accounting systems, covering ingestion, processing, and storage.
Put data quality and lineage controls in place so any number can be traced back to its source.
Build and manage training and evaluation datasets that power and measure AI models.
Lunon is an AI-native consulting firm in New York City backed by General Catalyst, embedding engineers inside client businesses to ship AI solutions that run without them. The team is young and fast-moving, with everyone owning what they ship end to end.
Build infrastructure for frontier AI agent research: environment execution, trajectory collection, and evaluation at scale.
Develop reusable tools, APIs, and pipelines for launching experiments, managing data, and inspecting results.
Turn research prototypes into tested systems, making experimentation faster, more reproducible, and cost-efficient.
HUD builds reliable, fair, and open infrastructure for AI data, making data valuable for creators and trustworthy for labs. The team is a quickly growing group of ~25 researchers, engineers, and operators backed by $16M from top VCs and YC.
Build and train generative 3D models that turn intent into manufacturable geometry across implicit, latent and B-rep targets.
Put physics in the loop with differentiable simulation and physics-driven optimization of generated shapes.
Own the training pipeline end to end and lead the research practice that decides what ships.
MimoLabs creates Mimo, an agentic system that designs geometry, simulates manufacturability, and routes production for physical objects. We are a small, VC-funded technical team based in Detroit and remote across the US.
Serve as a primary technical consultant, leading end-to-end AI/ML project implementations with clients.
Design, build, and maintain production-grade AI/ML pipelines, emphasizing CI/CD and advanced MLOps/AIOps practices.
Architect end-to-end AI/ML systems and manage infrastructure using Docker, orchestrators, and Databricks lakehouse.
Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies. We build modern Machine Learning systems for demand planning, budget forecasting, and custom AI solutions, fostering a data-nerd culture with values like ownership and a positive mindset.
Build post-training and evaluation systems for multimodal agents operating browsers and desktops.
Turn real workflows into environments, rewards, and experiments that drive reliable agent improvement.
Work directly with customers and research partners while maintaining a substantive research agenda.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models. The company has raised $150M in funding and fosters a culture of ownership and collaboration for ambitious AI teams.
Build evaluation frameworks for AI agents, including datasets and metrics for real-world task performance.
Design experiments to compare models and tools, identifying regressions and trade-offs.
Investigate agent failures and improve performance across models, harnesses, and tooling.
Gauntlet builds the financial systems of the future, operating across the onchain finance stack to offer vault products. They serve over $1.7B in client TVL across fintechs, protocols, exchanges, and capital allocators, combining traditional finance and crypto-native expertise.
Build and maintain AI agents using large language models to complete multi-step tasks within Cayuse products.
Develop and extend MCP servers and integrate AI features into existing applications through platform APIs.
Write unit and integration tests, participate in code reviews, and ship code through CI/CD pipelines.
Cayuse provides integrated technology solutions that empower organizations to conduct globally connected research, advancing impact in science and discovery. The company is a team of highly skilled, multidisciplinary professionals building a remote-first culture with a focus on customer and employee values.
Design, run, and analyse large-scale RL post-training experiments such as RLHF and RL from verifiable rewards.
Research and develop methods that improve out-of-distribution generalization, including reward design, task diversity, and regularization.
Build held-out evaluations to measure generalization and catch reward hacking, and turn successful research into production training recipes.
Cohere is a leading security-first enterprise AI company building cutting-edge foundation models and end-to-end products for businesses. We are a global team of researchers, engineers, and designers passionate about their craft, headquartered in Toronto with offices worldwide.
Own the delivery of client de-identification projects from raw data to accepted datasets.
Diagnose and fix pipeline issues, ensuring accuracy and reliability of output.
Build and adjust ingestion and transformation steps to turn raw exports into ready-to-use datasets.
Tonic.ai has been de-identifying sensitive data since 2018, serving enterprises like eBay and Qualcomm. They are a growing team focused on solving data privacy challenges for AI training with a collaborative and diverse culture.