Develop AI-based biomarkers using multimodal data including whole-slide images, clinical variables, and molecular data to predict patient outcomes and treatment benefit.
Contribute to self-supervised foundation models and downstream ML models such as multiple-instance learning, survival analysis, segmentation, and classification.
Improve model robustness and interpretability across scanners, institutions, and patient populations to build clinician trust.
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.
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.
Lead development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data.
Build interpretable, well-calibrated models and drive new methods from prototype through scalable implementation to clinical validation.
Collaborate with molecular biologists and clinical experts and mentor junior scientists.
Natera is a global leader in cell-free DNA testing, dedicated to oncology, women's health, and organ health. The team consists of highly dedicated professionals from world-class institutions who care deeply for the work and each other.
Design and implement cutting-edge machine learning algorithms and model architectures.
Collaborate with product, engineering, and analytics teams to integrate AI features across Atlassian products.
Conduct rigorous experimentation, model evaluation, and mentor emerging ML engineers.
Atlassian creates team collaboration software like Jira, Confluence, and Trello, aimed at unleashing the potential of every team. They are a distributed-first company with thousands of employees, emphasizing diversity, inclusion, and flexible work arrangements.
Design and build AI agents and automation to solve real problems across engineering, product, and delivery.
Partner with stakeholders to identify high-leverage opportunities and deliver end-to-end solutions.
Stay current with LLM and agentic frameworks to drive innovation in healthcare technology.
HealtheDGE provides AI-powered operational infrastructure for health insurance companies, helping them modernize operations. The company is experiencing strong market momentum and invests in its people, offering a collaborative culture focused on innovation.
Develop agentic research systems to automate mechanistic model development for biochemical and physiological processes.
Design and train machine learning models for in-context learning and few-shot adaptation on molecular and tabular data.
Collaborate with chemists and biologists to drive drug discovery decisions using AI models.
PostEra is building an AI-first biotech, using its Proton AI platform to accelerate the discovery of new medicines for patients. The company has a minimalist organizational structure, celebrating individual achievement through proportional compensation and collaboration toward delivering cures.
Develop, fine-tune, deploy, and monitor pretrained language models for healthcare applications
Work with messy unstructured EHR data, including clinical text and imaging, to generate real-world insights
Collaborate cross-functionally to translate clinical questions into data analytics requirements
Verana Health is a digital health company that delivers quality drug lifecycle and medical practice insights from an exclusive real-world data network in ophthalmology, neurology, urology, and oncology. Backed by Johnson & Johnson Innovation and GV, the company recently raised $150 million in Series E and maintains a diverse, remote-friendly culture with offices in San Francisco, New York City, and Knoxville.
Lead the development and optimization of machine learning models to detect and prevent AI security risks like prompt injection and jailbreaks.
Build reproducible training and evaluation pipelines on Reddit's ML platform, partnering with platform engineers to improve performance and reliability.
Set the technical vision and multi-quarter modeling roadmap, mentoring engineers and establishing best practices for responsible ML development.
Reddit is a community of communities, built on shared interests and authentic conversations, with 100,000+ active communities and 130 million daily active visitors. It is one of the internet's largest sources of information, fostering a culture of openness and trust.
Own model strategy and selection using rigorous benchmarks and statistical analysis.
Design and maintain evaluation methodologies for AI systems, including offline sets and LLM-as-judge frameworks.
Develop classification, fine-tuned, and agentic AI models to improve accuracy, cost, and latency.
A production agentic AI platform that builds and deploys advanced machine learning systems. The team is collaborative and values innovation, offering a remote work environment with high autonomy.
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.
Design and implement state-of-the-art ML models and training pipelines for robotics.
Develop efficient data/training strategies and evaluation frameworks for rapid experimentation.
Collaborate with engineering to optimize training infrastructure and deployment.
We're revolutionizing real-world automation by making robotic systems accessible to everyone. Our AI-powered platform brings software automation to physical spaces, and we're a small startup team working across the stack to solve customer problems.
Research, develop, and maintain production-quality machine learning code for materials science problems.
Collaborate with product, engineering, and research teams to transform AI ideas into scalable, customer-ready capabilities.
Evaluate and improve model performance, reliability, and interpretability across the full feature lifecycle.
The company develops materials-aware AI capabilities for scientific and industrial applications. They value autonomy, collaboration, and continuous learning in a remote environment.
Own the design and delivery of data engineering and clinical NLP pipelines that transform structured and unstructured EMR data into standardized clinical data.
Develop hybrid approaches using rules, dictionaries, deep learning, and LLMs for clinical entity extraction, normalization, and context interpretation.
Partner with clinicians to define schemas, validate OMOP CDM mappings, and ensure production reliability for transplant-focused data.
CareDx is a precision medicine diagnostics company advancing care in transplant, specialty oncology, and cell therapy through molecular diagnostics, digital health solutions, and AI-powered analytics. The company fosters a collaborative culture where every employee contributes to innovations that transform patient care.
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.
Train, evaluate, and iterate on ML models for customer feedback tasks with rigorous experimentation.
Build and maintain LLM-powered features including retrieval pipelines, reranking systems, and insight generation.
Contribute to evaluation frameworks, semantic search, and collaborate with engineering on model integration.
We help large brands like Uber, Amazon, and Wise put customers at the centre of everything they do using our AI-powered Customer Experience Intelligence platform. Our collaborative, growth-driven culture values trust, ownership, and humility as we build the next generation of customer feedback technology.
Build and ship AI methods for federal research data with LLMs, NLP, RAG, knowledge graphs.
Benchmark AI against baselines and human reviewers to prove gains in accuracy, reliability, efficiency, cost.
Set standards for trustworthy AI and mentor data scientists through code review and guidance.
ARI builds AI methods for federal research-funding and administrative data, with clients including the NIH. It is a small multidisciplinary team of scientists, analysts, economists, and data scientists experienced with NIH data.
Build and own training pipelines for ML models, including data prep, fine-tuning, and experiment tracking.
Develop inference and serving layer with model gateway, routing, caching, and cost optimization.
Own release path for model-layer artifacts with canary deployments and monitoring for regressions.
Horizon3 is a fast-growing cybersecurity company that provides autonomous pentesting through its NodeZero platform. The team is a fusion of former special operations cyber operators, startup engineers, and cybersecurity practitioners, fostering a culture of respect, collaboration, and ownership.
Build and improve computer vision and 3D perception models that detect and assess infrastructure from LiDAR and imagery.
Develop LLM-powered products like agentic workflows, MCP servers, and AI copilots for customer tools.
Own models end-to-end, from data and training through evaluation, deployment, and monitoring in production.
Cyvl is a Physical AI company building purpose-built sensors, computer vision, and AI to turn every drive into current data for infrastructure. We're a fast-moving Boston startup with 500+ cities using our platform, and a culture of ownership, intensity, and care.
Lead the AI/ML organization behind the immigration case management platform, including document understanding and extraction.
Manage and grow a team of ML engineers, setting technical direction on build-vs-buy for AI/ML capabilities.
Be accountable for cost, quality, and throughput of every model in production, with recognized depth in the field.
Envoy Global innovates in global immigration, combining a tech platform with holistic service to streamline immigration for employers and individuals. The company is scaling its AI/ML organization and seeks a Director to lead document understanding and automation.
Develop advanced ML models and agentic workflows to accelerate model development.
Use AI-assisted tools like Claude and Cursor to investigate model behavior and automate analysis.
Set technical direction, mentor engineers, and raise the bar for modeling rigor.
Reddit is a community of communities, built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.