Develop, fine-tune, and optimize self-hosted large language models for personalized AI conversations.
Work across the full model development lifecycle, from dataset preparation to GPU-based experiments and production deployment.
Collaborate with cross-functional teams to enhance conversational quality, memory, and multilingual interactions.
This company builds innovative consumer-facing AI products powered by self-hosted large language models. They operate a remote-first culture across Europe, emphasizing collaboration, continuous learning, and technical excellence.
Design, deploy, and optimize large-scale ML systems for retrieval, ranking, and LLM-powered features.
Lead ML projects from concept to production, improving answer relevance and user experience.
Collaborate with Product, Data Science, and Engineering teams to shape technical direction.
The hiring partner is a technology company focused on building advanced AI systems that help users discover relevant answers from large collections of online conversations. It values innovation, collaboration, and responsible AI development, offering a remote work environment within the United States.
Develop deep understanding of customer problems and map them to Cohere solutions as a trusted technical advisor.
Lead design and deployment of customer pilots, owning the technical narrative from discovery to business case.
Collaborate with Product and Engineering teams and partners to scale solutions and gather customer feedback.
Cohere is a leading security-first enterprise AI company building cutting-edge foundation AI models and products for real-world business problems. It is a global team of researchers, engineers, and designers headquartered in Toronto with offices worldwide, committed to driving AI adoption.
Lead exploration and application of Large Language Models and Generative AI into core product lines.
Translate the latest research into high-performing systems that enhance customer experiences.
Collaborate with cross-functional teams to set strategy and deploy ML solutions with real business impact.
Replicant is the industry leader in voice AI for customer service, providing automated phone bots for enterprises like AAA and Fanatics. Founded in 2017, the company is backed by top-tier Silicon Valley investors, has offices in the SF Bay Area, Toronto, and New York, and maintains a distributed team across the US and Canada.
Identify and implement AI/ML opportunities across products, including generative AI and LLM applications.
Build, evaluate, deploy, and maintain machine learning models in production.
Collaborate with product managers, engineers, and experts to translate business challenges into scalable ML solutions.
The company applies machine learning and AI to real-world business and product challenges. It fosters a collaborative, remote-first environment centered on technical learning, innovation, and professional development.
Build new functionality within a fast-moving product, taking ambiguous ideas from design to rigorous experimentation within weeks.
Conduct deep-dive research into state-of-the-art LLM orchestration, retrieval strategies, and data-driven personalization.
Build production-grade ML systems at Twilio scale, leveraging the latest AI development stacks to focus on core differentiation.
Twilio is a cloud communications platform that enables businesses to create personalized customer experiences through APIs and AI-driven solutions. The company is remote-first, serving hundreds of thousands of businesses, and fosters a culture of connection, inclusion, and innovation.
Fine-tune open-weight LLMs using SFT, LoRA/QLoRA, and post-training methods such as DPO and GRPO to shape model behavior for 50M+ users.
Diagnose model failures and turn them into experiments, building training data and evals that directly influence production models.
Ship to production, speed up the training loop, and collaborate with Product, Web, and DevOps teams.
EverAI is building the world's largest AI companionship platform, redefining relationships for millions. With ~100 people, it's a fully remote, hardworking team with an ownership-driven culture and a track record of scaling products from 0 to IPO.
Build and ship LLM-powered applications and AI agent workflows.
Design systems for reasoning, planning, memory, tool use, and multi-step execution.
Optimize AI systems for quality, latency, and cost, and establish production practices for observability and evaluation.
Gina's Tech Jobs is a technology recruiting firm that connects companies with engineering and IT professionals. They focus on remote and onsite placements, offering personalized recruitment services and career support.
Own end-to-end ML systems from data and training to evaluation, inference, and deployment.
Build and evolve training, fine-tuning, and inference infrastructure for large models.
Partner with research and application engineering to turn model capabilities into product improvements.
Next Step Systems is an IT recruiting and staffing agency that connects companies with machine learning and technology talent. They specialize in placing candidates in remote and on-site roles across the United States and emphasize a fast-paced, results-oriented culture.
Design, build, and maintain scalable real-time ML services for ranking and personalization.
Analyze user behavior and run A/B tests to improve product discovery and engagement.
Collaborate with engineers, data specialists, and business stakeholders to drive measurable outcomes.
The company is an AI-driven e-commerce platform that uses machine learning to power smarter search, discovery, and shopping experiences. It maintains a collaborative, remote-first culture that values ownership, inclusion, and meaningful impact.
Design and improve AI systems for extracting structured reasoning signals from technical interviews.
Build LLM-powered pipelines for semantic analysis, behavioral feature extraction, and evaluation.
Develop scoring, calibration, and benchmarking methods grounded in measurable evidence.
APADCode is an early-stage AI startup building an AI-powered technical interview platform that evaluates how candidates reason and solve problems. The company is a small, early-stage team with a culture of high ownership, direct collaboration, and minimal bureaucracy.
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.
Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models.
Deploy and scale LLM inference services, manage GPU compute, and optimize workload scheduling and utilization.
Automate deployment workflows with infrastructure-as-code and CI/CD, and collaborate with ML engineers to move models to production.
Hire Hangar connects top talent with vetted employers and offers competitive pay and real growth opportunities. It focuses on matching remote contractors with fast-growing global companies.
Lead the end-to-end machine learning lifecycle, from data preparation and model training to evaluation, inference, and deployment.
Build and optimize scalable training, fine-tuning, and inference pipelines for large-scale models on GPU infrastructure.
Establish production best practices for model quality, reliability, safety, and measurable performance improvements.
This company builds production-grade machine learning systems and large-scale GPU-powered inference pipelines. It is a fully remote team that values technical rigor, collaboration, and hands-on engineering.
Design, train, and deploy ML models for text classification, entity extraction, and topic detection.
Build LLM-powered features including RAG, prompt design, and agentic workflows.
Collaborate daily with US-based engineers and productionize models as monitored services.
Tech Holding is a full-service consulting firm that delivers predictable outcomes and high-quality solutions to clients from startups to Fortune 50 companies. The culture is built on deep expertise, integrity, transparency, and dependability, with a team of experienced industry professionals.
Build the component layer around our layout synthesis engine, including API contracts, services, and evaluation gates.
Turn model retraining into a one-command job with built-in benchmarks and readable results for the whole team.
Own latency and cost budgets for learned capabilities and partner with infrastructure engineers on MLOps and deployment.
Higharc is a VC-backed startup that is changing how new homes are designed and built using spatial AI and generative floor plan technology. The company is fully remote, has raised over $175M, and values flexibility, collaboration, and asynchronous deep work.
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.
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 and maintain production-grade machine learning infrastructure for AI governance and trust, including model serving, evaluation, and monitoring.
Develop low-latency inference systems, model registries, and evaluation pipelines for reliable model lifecycle management.
Design and implement red-team testing, drift monitoring, and continuous retraining loops using telemetry and audit data.
The company builds AI governance and trust infrastructure, including production ML systems for AI agents. It offers a remote-first, international engineering organization with a focus on security and reliability.