Develop and refine features for deep learning models using large-scale customer and behavioral datasets.
Implement model architecture changes informed by recent academic research from venues like NeurIPS.
Optimize model training pipelines for efficiency and scalability while collaborating with client teams.
OpenTeams builds AI that empowers, offering energy-efficient and cost-effective models with a commitment to open source. The company values freedom, teamwork, accountability, and quality, and reinvests 3% of profits into the open-source community.
Build and iterate on consumer-facing AI features powered by large language models (LLMs) and generative AI systems
Collaborate with engineers across the AI stack including prompt engineering and agentic workflow optimization
Run structured experiments and monitor production AI systems to optimize latency, cost, and scalability
Quora is a global knowledge sharing platform with over 300M monthly unique visitors, connecting people to share insights and learn. Poe provides a platform for users to chat and build with AI language models. They are a remote-first company with a culture rooted in transparency and experimentation.
Design, develop, and deploy ML models, including large language models, for various NLP tasks.
Collaborate with cross-functional teams to gather requirements, define architectures, and iterate on model development.
Stay up-to-date with latest research and contribute to best practices for responsible ML development.
Reddit is a community of communities built on shared interests, passion, and trust, hosting the most open and authentic conversations on the internet. With over 100,000 active communities and approximately 126 million daily active users, it is one of the internet's largest sources of information, fostering a culture of authenticity and community.
Design and maintain LLM-powered backend services using Python and FastAPI.
Implement retrieval-augmented generation (RAG) for structured and unstructured fleet data.
Optimize retrieval accuracy, latency, and hallucination rates through automated evaluation pipelines.
Datakrew revolutionizes EV fleet intelligence with IoT and AI solutions. They aim to serve one million EVs within 5 years and cultivate a mission-driven culture.
Develop and improve NLP systems and language model-powered experiences.
Fine-tune and optimize language models for domain-specific use cases and build evaluation frameworks.
Deploy and maintain production-grade ML systems on GPU infrastructure with a focus on scalability and safety.
BetterHelp removes barriers to therapy and makes mental health care accessible globally. Founded in 2013, it is now the world's largest online therapy service with over 30,000 licensed therapists, and it invests deeply in employee well-being and professional development.
Lead technical discovery with foundation model labs, frontier AI teams, and large enterprises to understand model objectives and constraints.
Design end-to-end solutions across the post-training stack including SFT data curation, RLHF/DPO pipelines, custom benchmarks, and LLM-as-judge systems.
Author technical proposals, run workshops and POCs, and serve as ongoing technical advisor during delivery.
Innodata is a global data engineering company focused on enabling responsible AI advancement through data, evaluation frameworks, and human expertise. With a 36+ year legacy, they provide high-quality data solutions to foundation model labs, hyperscalers, and enterprise AI teams.
Own end-to-end Machine Learning (ML) system execution including data pipelines, training, and deployment.
Fine-tune and adapt models using state-of-the-art methods like LoRA and DPO.
Architect scalable inference systems and collaborate closely with application engineering.
This company develops advanced production-grade machine learning systems. The team is small and high-trust, with a culture of ownership and pragmatism.
Design, build, and ship ML models that power content generation and quality eval scoring for Canva's generated element and template library.
Own the full ML lifecycle — from data pipelines and training through to deployment, monitoring, and iteration.
Partner with Content Engine, CORE AI Research, AI Media, and Discovery teams to align ML work with the broader content strategy.
Canva is redefining how the world experiences design with its intuitive design platform. We serve hundreds of millions of users globally and foster a culture of flexibility, inclusion, and innovation.
Develop scalable ML pipelines across the full lifecycle and champion responsible AI for content understanding models and signals in production.
Provide technical leadership and mentorship to ML engineers and software engineers, setting technical standards and conducting design reviews.
Build evaluation and quality monitoring systems for content understanding signals using state-of-the-art LLM-as-judge practices.
Reddit is a community of communities built on shared interests, passion, and trust, home to the most open and authentic conversations on the internet. With 100,000+ active communities and approximately 126 million daily active unique visitors, it is one of the internet's largest sources of information.
Build models and data products that go from prototype to production, including generative models and subscriber-behavior predictions.
Dig into large, messy datasets to uncover trends and patterns, and contribute to the core Python data science library.
Build LLM-powered pipelines and agents, with comprehensive evals to validate model responses.
DevSavant is an operating partner for startups and growth-stage companies, helping them turn ambition into execution. With over 8 years in venture-backed ecosystems, they are trusted to accelerate delivery and scale teams efficiently.
Build and ship AI features end-to-end, from model to system to user experience.
Design and iterate on prompts, tools, memory, and agent workflows for real-world reliability.
Debug full-stack issues and optimize for latency, cost, and production performance.
A1 builds a proactive smart assistant for everyday users, bringing intelligence to conversations, errands, organizing, and workflows with minimal prompting. The team is small, world-class, and focuses on rapid iteration and shipping high-quality AI products.
Design, develop, and deploy production-grade AI-powered backend systems integrating LLMs and traditional ML models.
Integrate and optimize vector databases for RAG pipelines, and write clean, well-structured Python code.
Debug complex cross-layer issues and collaborate with product and engineering teams to deliver cohesive solutions.
We are a fast-growing product company integrating cutting-edge AI capabilities into our core offering to deliver exceptional value to customers. Our small, fast-moving team works on practical, real-world AI applications with high autonomy.
Build, train, and improve machine-learning models for production use.
Develop evaluation and feedback loops that measurably improve model performance over time.
Evaluate new models and techniques, bringing the best into production.
Allocate transforms private market investing by enabling RIAs and family offices to discover, model, and manage private market exposure. The company provides a platform with curated fund and co-investment opportunities, and values meritocracy, civil discourse, and continuous improvement.
Build, ship, and own product features end-to-end using cutting-edge AI/ML techniques.
Apply classical ML and LLM-based approaches like RAG, prompt engineering, and fine-tuning to enhance the audit and risk platform.
Collaborate with cross-functional teams in an Agile environment to deliver scalable, production-quality code.
Optro is a leading audit, risk, ESG, and InfoSec platform trusted by over 50% of the Fortune 500. The company has been named one of the 500 fastest-growing tech companies in North America for seven consecutive years, fostering a culture of innovation and collaboration.
Build and improve ML components across data, training, evaluation, and inference.
Implement evaluation and testing to understand model behavior.
Debug model issues, performance problems, and production incidents.
This company builds core ML components for large-scale production systems. They emphasize real-world learning, iteration, and collaboration with senior engineers.
Design, build, and operate multi-agent workflows and tool-enabled agents for resilient production pipelines.
Architect and maintain end-to-end RAG systems covering document ingestion, chunking, embedding, retrieval, and answer synthesis.
Define and own evaluation frameworks for generative outputs, including automated metrics, LLM-as-judge, and hallucination detection.
Mitratech builds world-class products that simplify operations in Legal, Risk, Compliance, and HR functions. With over 35 years of experience, we serve 20,000 client companies globally, including 30% of the Fortune 500, and foster a diverse, inclusive culture centered on individual excellence and learning.
Design and develop machine learning models for localization workflows, including machine translation and LLM finetuning.
Implement and optimize models using Python, TensorFlow, and deploy via Docker and AWS services.
Evaluate and select ML techniques, perform statistical analysis, and maintain clear documentation.
Welo Global is a leader in multilingual AI, technology, and content solutions serving over 2,000 clients in 300 languages. The company combines globally scaled multilingual infrastructure with a network of over 500,000 linguists and domain experts, backed by seven ISO certifications.
Design and maintain scalable ML infrastructure including data pipelines, training workflows, and model deployment systems.
Own end-to-end ML lifecycle operations, ensuring reliable delivery of models into production at scale.
Implement monitoring, telemetry, and feedback loops for ML models running across large-scale device fleets.
Our partner company develops ML systems for connected hardware products used by customers worldwide. They operate in a fast-paced, product-driven environment with a collaborative and technically ambitious culture focused on real-world ML impact.
Research and implement state-of-the-art techniques to accelerate AI inference: quantization, sparsity, distillation, speculative decoding, and caching.
Partner closely with hardware and compiler teams to ensure algorithmic improvements translate to real gains on custom silicon.
Build profiling tools and comprehensive benchmarking frameworks to measure model quality and efficiency.
EnCharge AI is building the next generation AI platform using novel in-memory-computing architecture. The team consists of experienced AI researchers, silicon & systems engineers, and architects backed by leading investors.