Research Tools Platform

Next-generation scientific computing infrastructure, experiment tracking, and collaboration platforms accelerating AI research. Built-in reproducibility frameworks, distributed training orchestration, and knowledge management systems for academic and industry research teams.

10K+
Active Researchers
1M+
Experiments Tracked
500+
Institutions
99.99%
Uptime SLA

Platform Overview

Accelerating Scientific Discovery

Our Research Tools Platform provides comprehensive infrastructure for modern AI research, from experiment design to publication. Through integrated experiment tracking, hyperparameter optimization, distributed training orchestration, and collaborative notebooks, we eliminate infrastructure friction enabling researchers to focus on innovation and discovery.

Built for academic institutions, corporate research labs, and AI startups, our platform ensures full reproducibility with versioned datasets, model checkpoints, code snapshots, and environment configurations. Every experiment is traceable, auditable, and shareable, supporting peer review, collaboration, and scientific transparency. Seamless integration with HPC clusters, cloud GPUs, and on-premise infrastructure ensures flexibility across diverse research environments.

Experiment Tracking

  • Automatic logging of hyperparameters, metrics, and artifacts
  • Interactive visualization of training curves and model performance
  • Experiment comparison, filtering, and search across projects
  • Model registry with versioning and deployment tracking
  • Provenance tracking for full reproducibility and auditability

Distributed Training Orchestration

  • Multi-GPU and multi-node training with automatic scaling
  • Integration with SLURM, Kubernetes, Ray for job scheduling
  • Spot instance management and fault-tolerant checkpointing
  • Hyperparameter sweeps with Bayesian optimization and PBT
  • Resource utilization monitoring and cost optimization

Collaborative Research

  • Shared project workspaces with role-based access control
  • Collaborative Jupyter notebooks with real-time editing
  • Version control integration (Git, DVC) for code and data
  • Comment threads and annotations on experiments and results
  • Publication-ready plots, tables, and LaTeX export

Dataset Management

  • Versioned datasets with deduplication and compression
  • Efficient data loading with caching and prefetching
  • Data provenance tracking from raw to preprocessed
  • Support for petabyte-scale datasets across S3, HDFS, NFS
  • Privacy-preserving data sharing and access controls

Reproducibility and Provenance

  • One-click experiment reproduction with exact environments
  • Docker and Conda environment capturing and versioning
  • Git commit linking and dependency freezing
  • Artifact storage for models, checkpoints, predictions
  • Audit trails for regulatory compliance and peer review

Integration Ecosystem

  • PyTorch, TensorFlow, JAX, scikit-learn integration
  • HuggingFace Transformers and Diffusers compatibility
  • Weights & Biases, MLflow, TensorBoard interoperability
  • GitHub Actions, GitLab CI/CD pipeline integration
  • Slack, email, and webhook notifications for job status

Join the Research Tools Team

Build infrastructure empowering researchers to make breakthrough discoveries in AI and scientific computing

Hiring planned → Coming soon

Principal Engineer, Research Infrastructure

Location: Philadelphia, PA or Remote
Compensation: $205,000 - $290,000 + equity
Type: Full-time

Architect and build scalable research infrastructure supporting distributed training, experiment tracking, and collaborative research at academic and industry scale. Design systems enabling 10K+ researchers to run millions of experiments across cloud and on-premise GPU clusters.

Core Responsibilities

  • Design and implement distributed training orchestration systems supporting multi-GPU, multi-node workloads across heterogeneous clusters
  • Build experiment tracking infrastructure handling millions of runs with sub-second query latency for metrics, hyperparameters, and artifacts
  • Architect storage systems for petabyte-scale datasets, model checkpoints, and training artifacts with deduplication and efficient retrieval
  • Develop job scheduling and resource allocation algorithms optimizing GPU utilization, cost efficiency, and researcher productivity
  • Implement fault-tolerant checkpointing, spot instance management, and automatic failure recovery for long-running training jobs
  • Build monitoring and observability systems tracking cluster health, job progress, resource utilization, and infrastructure costs
  • Collaborate with research teams to understand workflows and design APIs, SDKs, and CLI tools for seamless integration

Required Qualifications

  • BS/MS in Computer Science, Engineering, or related field with 8+ years of infrastructure engineering for ML/HPC workloads
  • Deep expertise in distributed systems, job scheduling, and resource orchestration using Kubernetes, SLURM, or Ray
  • Strong systems programming skills in Go, Rust, or C++ with experience building high-performance, scalable infrastructure
  • Proven track record designing and operating ML platforms supporting hundreds of researchers and thousands of experiments
  • Experience with cloud infrastructure (AWS, GCP, Azure) and GPU-accelerated computing (NVIDIA A100, H100, AMD MI300)
  • Understanding of ML training workflows, distributed training frameworks (PyTorch DDP, DeepSpeed, Horovod), and model serving
  • Familiarity with storage systems (S3, HDFS, Lustre, Ceph) and database technologies (PostgreSQL, TimescaleDB, ClickHouse)

Preferred Qualifications

  • PhD in Computer Science or experience as staff engineer at leading ML infrastructure companies (Google, Meta, Microsoft)
  • Contributions to open-source ML infrastructure projects (Kubeflow, Ray, MLflow, Weights & Biases, Determined AI)
  • Experience building HPC clusters or working at national labs with supercomputing infrastructure
  • Background in hyperparameter optimization algorithms (Bayesian optimization, PBT, ASHA)
  • Understanding of GPU networking (InfiniBand, NVLink, RoCE) and high-performance interconnects
  • Track record optimizing infrastructure costs and improving GPU utilization at scale

Why This Role Matters

Your infrastructure will accelerate breakthrough discoveries in AI, healthcare, climate science, and fundamental research. Build systems empowering the next generation of researchers, eliminating infrastructure barriers to scientific innovation and discovery.

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Senior Research Software Engineer

Location: Philadelphia, PA or Remote
Compensation: $165,000 - $230,000 + equity
Type: Full-time

Build research productivity tools enabling reproducible, collaborative, and efficient ML research. Design APIs, SDKs, and developer experiences that researchers love, integrating seamlessly with PyTorch, TensorFlow, and modern ML workflows.

Core Responsibilities

  • Design and implement Python SDKs and APIs for experiment tracking, hyperparameter logging, and artifact management
  • Build integrations with popular ML frameworks (PyTorch, TensorFlow, JAX, HuggingFace) ensuring zero-friction adoption
  • Develop collaborative Jupyter notebook environments with real-time editing, versioning, and sharing capabilities
  • Implement dataset versioning and data loading libraries optimizing I/O performance for large-scale training
  • Create visualization dashboards for training metrics, model comparisons, and hyperparameter analysis using React and D3.js
  • Build CLI tools and automation scripts for common research workflows (sweeps, reproducibility, model deployment)
  • Collaborate with research users to gather feedback, prioritize features, and improve developer experience

Required Qualifications

  • BS/MS in Computer Science or related field with 6+ years of software engineering experience building developer tools or ML infrastructure
  • Strong Python programming skills with experience designing APIs, SDKs, and libraries used by external developers
  • Deep understanding of ML research workflows, experiment tracking, and reproducibility challenges
  • Experience with PyTorch, TensorFlow, or JAX and familiarity with distributed training and model optimization
  • Proficiency in full-stack development with React, TypeScript, and modern web technologies
  • Understanding of data engineering, database systems, and efficient data loading for ML training
  • Excellent communication skills with ability to engage researchers, gather requirements, and provide technical support

Preferred Qualifications

  • PhD in Computer Science or ML research background with first-hand experience in academic or industry research
  • Contributions to open-source ML libraries (PyTorch, TensorFlow, HuggingFace, scikit-learn)
  • Experience building experiment tracking tools (MLflow, Weights & Biases, TensorBoard, Neptune)
  • Background in data visualization, interactive dashboards, and user interface design
  • Familiarity with containerization (Docker), environment management (Conda), and reproducibility tools (DVC)
  • Track record engaging with research communities through documentation, tutorials, and conference talks

Why This Role Matters

Your tools will be used daily by thousands of researchers advancing the frontiers of AI, science, and technology. Build products that eliminate friction, improve reproducibility, and accelerate the pace of discovery in machine learning research.

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Director of Product, Research Platform

Location: Philadelphia, PA
Compensation: $185,000 - $255,000 + equity
Type: Full-time

Own product strategy for Research Tools Platform, driving adoption among academic institutions, corporate research labs, and AI startups. Translate researcher needs into product features enabling reproducible, collaborative, and efficient ML research at scale.

Core Responsibilities

  • Define and execute product strategy for Research Tools Platform, balancing academic research needs with enterprise requirements
  • Own P&L for Research Platform including revenue targets, pricing strategy, customer acquisition, and retention metrics
  • Lead customer discovery with academic labs (MIT, Stanford, Berkeley), corporate research (Google AI, Meta FAIR), and AI startups
  • Collaborate with engineering teams to prioritize features including experiment tracking, distributed training, collaboration tools, and integrations
  • Build product roadmap addressing reproducibility, scalability, cost optimization, and researcher productivity challenges
  • Drive go-to-market execution partnering with academic outreach, developer relations, and enterprise sales teams
  • Establish product metrics measuring adoption, engagement, experiment volume, research productivity, and customer satisfaction
  • Represent platform at ML conferences (NeurIPS, ICML, MLSys), publish blog posts, and build relationships with research community

Required Qualifications

  • 8+ years of product management experience with 5+ years in developer tools, ML infrastructure, or research platforms
  • Proven track record owning P&L and driving adoption for technical products serving researchers and data scientists
  • Deep understanding of ML research workflows, experiment tracking, distributed training, and reproducibility challenges
  • Technical fluency to engage with researchers and engineers, understanding ML frameworks and infrastructure requirements
  • Experience with both academic (universities, national labs) and industry research (tech companies, AI labs) customers
  • Strong quantitative skills with expertise in product analytics, cohort analysis, and researcher productivity metrics
  • Executive presence to engage principal investigators, research directors, and present at academic conferences

Preferred Qualifications

  • PhD in Computer Science, ML, or quantitative field with first-hand research experience
  • Background in ML infrastructure companies (Weights & Biases, Determined AI, Comet, Neptune) or cloud ML platforms
  • Experience with academic partnerships, grant programs, and research community engagement
  • Technical degree with hands-on ML research or publications at top-tier conferences
  • Track record building 0-to-1 developer tools or research platforms with strong community adoption
  • Network in ML research community through conferences, workshops, or academic collaborations

Why This Role Matters

Shape how researchers conduct ML experiments, ensuring reproducibility, collaboration, and accelerated discovery. Build products supporting breakthrough research in AI, medicine, climate science, and fundamental physics at leading institutions worldwide.

Express Interest