Technologies
Big Data
Developers using Activeloop Deep Lake

Developers using Activeloop Deep Lake

Activeloop Deep Lake is a data lake storage solution designed for machine learning workflows, offering version control, efficient storage of unstructured data, seamless integration with ML frameworks, and optimized data streaming capabilities.
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Developers using Activeloop Deep Lake

NAME
contact
DESIGNATION
COUNTRY
Company
Total tENURE
Kunal Sonawane
Software Engineer
United States Country Flag Icon
United States
Infosys Company Logo
Infosys
3 years
Naveen Gajja
Lead Big Data Engineer
United States Country Flag Icon
United States
Effectual Company Logo
Effectual
18 years
Venkatesh Pati
Sr Data Engineer Big Data Developer
United States Country Flag Icon
United States
Databricks Company Logo
Databricks
7 years
Stepan Riha
Tech Lead Senior Software Engineer
United Kingdom Country Flag Icon
United Kingdom
CGI Company Logo
CGI
30 years
Bhavesh Sharma
Founding Engineer
United States Country Flag Icon
United States
Red Hat Company Logo
Red Hat
5 years
Kumar
Senior Java Developer
United States Country Flag Icon
United States
Infosys Company Logo
Infosys
2 years
Zhaoyu Luo
Software Engineer
United States Country Flag Icon
United States
Snowflake Company Logo
Snowflake
10 years
Trey Hakanson
Senior Software Engineer
United States Country Flag Icon
United States
Databricks Company Logo
Databricks
8 years
A Khan
Senior Java Developer
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United States
AMD Company Logo
AMD
4 years
Chandra S
Aws Cloud Engineer
United States Country Flag Icon
United States
Verizon Company Logo
Verizon
4 years
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8,914
results
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892
NAME
contact
DESIGNATION
COUNTRY
Company
Total tENURE
Pradeep Batham
Senior DevOps Engineer
India Country Flag Icon
India
Estapar Company Logo
Estapar
2 years
Ye W
Senior Consultant
United States Country Flag Icon
United States
Infopro Digital Company Logo
Infopro Digital
3 years
Guillaume NICOLAS
Head of Development
France Country Flag Icon
France
Amazon Company Logo
Amazon
3 years
Steven Moy
Head of App Development
United States Country Flag Icon
United States
Astreya Company Logo
Astreya
18 years
Patrice Lavocat
Senior Backend Developer
France Country Flag Icon
France
Lacework Company Logo
Lacework
10 years
Sachin Jani
VP Web Platforms
India Country Flag Icon
India
UPS Company Logo
UPS
3 years
Chris Jefferies
Senior Software Engineer
United States Country Flag Icon
United States
Lubbock County Company Logo
Lubbock County
25 years
Gabe Church
Principal Data Engineer
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United States
Atlassian Company Logo
Atlassian
3 years
Daniel Easterman
Lead Software Developer
United Kingdom Country Flag Icon
United Kingdom
OpenText Company Logo
OpenText
7 years
Matthew Radcliffe
Database Operations Manager
United States Country Flag Icon
United States
S&P Global Company Logo
S&P Global
3 years
Showing 10 of
8,914
results
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892

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8,914
developers actively working with
Activeloop Deep Lake
technology, including economic buyers data for each account, complete with verified contact information, role tenure, company context, and adoption signals.
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Companies using Activeloop Deep Lake

Technology
is any of
Activeloop Deep Lake Technology Logo/Icon
Activeloop Deep Lake
company
COUNTRY
Tech confidence score
REVENUE
# Tech JOB POSTINGS
Birmingham City University
United Kingdom Country Flag Icon
United Kingdom
-
2
Matterport
United States Country Flag Icon
United States
$111.2M
68

Want access to the complete company list?

Unlock the full database of
2
companies actively hiring with
Activeloop Deep Lake
technology, including firmographic data,
8,914
developer profiles working on that technology, and direct contacts to engineering leaders within your target accounts.
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What would you like to do with developer-level contact data that are users of Activeloop Deep Lake?

Build my target developer list or assign economic buyers leads to my sales team

Transform your desired technology user data into actionable sales territories by combining firmographic ICP criteria with real-time technology adoption signals. Traditional account assignment based solely on company size and industry leaves money on the table—successful DevTool sales teams prioritize accounts showing active technology expansion signals.

Strategic account prioritization framework

Building an effective Total Addressable Market (TAM) requires more than basic firmographic filters. Companies using your desired technology represent varying levels of buying intent depending on their implementation stage, team growth, and technology stack evolution. Learn our complete framework for building DevTool ICP account lists to establish the foundation for strategic account segmentation.

Confluent ICP scoring example illustrating core, broader, and relevant universe tiers based on Kafka adoption and data streaming scale

Standard ICP criteria—geography, industry, company size, revenue—only provide baseline qualification. High-performing sales teams layer technology hiring signals on top of firmographic data to identify accounts actively expanding their technical capabilities. Companies hiring  for your desired technology engineers or architects signal active investment in the technology stack, indicating higher purchase intent and budget availability.

In-market account identification and assignment

Priority account assignment should factor in recent technology hiring patterns as a proxy for market timing. Companies posting jobs for Redis engineers, Kubernetes specialists, or React developers demonstrate active technology expansion—making them significantly more likely to evaluate complementary tools within 90 days.

Our LinkedIn outreach playbook details the specific process for identifying and assigning these in-market accounts to sales teams. This approach increases meeting acceptance rates by 40% compared to generic outbound because prospects are already in active buying mode.

Territory assignment best practices

Tier 1 accounts: Companies using your desired technology with recent hiring activity for related roles. These accounts get immediate sales attention with personalized outreach referencing their specific technology initiatives and hiring needs.

Tier 2 accounts: Established desired technology users without recent hiring signals but strong firmographic fit. Assign these accounts for longer-term nurture campaigns and quarterly check-ins to monitor technology expansion signals.

Tier 3 accounts: Companies using your desired technology with weaker ICP fit or unclear expansion signals. Route these accounts to inside sales or marketing-qualified lead campaigns until stronger buying signals emerge.

Refresh account assignments monthly based on new hiring signals and technology adoption data. Companies can move between tiers quickly as their technology needs evolve, and sales territories should reflect these dynamic market conditions rather than static demographic assignments.

The combination of your desired technology usage data and hiring intelligence creates a predictive framework for sales success, ensuring your team focuses energy on accounts most likely to convert within the current quarter.

Learn more

Run marketing Campaigns

Leverage your desired technology user data to create targeted campaigns across three distinct audience levels: companies, developers (practitioners), and economic buyers. Each audience type requires different messaging, channels, and campaign strategies to maximize conversion rates.

Multi-level audience targeting

Companies: Target organizations using your technology of choice for account-based marketing approaches. Focus on company-level signals, firmographics, and technology stack intelligence to build high-intent prospect lists.

Developers (contacts): Reach practitioners who directly implement and use chosen technology. These technical decision-makers influence tool adoption and can become internal champions for your solution.

Economic Buyers (contacts): Target executives and budget holders at companies using your desired technology. While they may not use the technology directly, they control purchasing decisions and strategic technology investments.

Campaign strategies by audience type

ABM Google/LinkedIn Ads to your TOFU audience: Run account-based display campaigns targeting companies using containerization technologies like Docker or Kubernetes. Create awareness-stage content about DevOps optimization, infrastructure costs, or developer productivity to capture early-stage interest from decision-makers.

Invite developers to topical webinars: Host technical webinars for Redis users about database optimization, caching strategies, or microservices architecture. Developers using Redis are likely interested in performance engineering topics that showcase your platform's capabilities in a educational, non-sales context. Leading DevTools like Galileo and Camunda use this strategy effectively—see how they leverage expert-led sessions to grow their TOFU audience by educating and nurturing developer communities around emerging technologies.

LinkedIn outbound campaigns to developers or economic buyers: Execute targeted LinkedIn outreach to practitioners and buyers at companies using complementary technologies. See how Kubegrade leveraged Kubernetes user data to run successful LinkedIn and email campaigns, or follow our proven LinkedIn outreach playbook that helped Unstructured book meetings with economic buyers.

Competitor email campaigns based on competitor technology: Target companies using competing solutions like MongoDB (if you're in the database space) or Elasticsearch (for search solutions). Craft messaging around migration benefits, performance comparisons, or feature gaps that position your solution as the superior alternative.

Complimentary technology campaigns: Run campaigns to companies using GraphQL (if you provide API tools) or React (for frontend development solutions). Focus messaging on how your product enhances their existing technology investments rather than replacing them—creating additive value propositions.

Technical content nurture campaigns to developers: Send regular technical newsletters to PostgreSQL users featuring database optimization tips, query performance guides, or architectural best practices. This builds relationship equity with practitioners who influence purchasing decisions while demonstrating your platform's technical depth.

Campaign execution framework

Each campaign type works best when aligned with the prospect's technology maturity and buying stage. Companies actively expanding their technology usage often have budget allocated for complementary solutions, making them higher-intent prospects than those just beginning adoption.

Combine multiple campaign types for maximum impact: start with educational content to developers, then retarget engaged prospects with ABM campaigns to economic buyers at the same companies. This multi-touch approach increases conversion rates while building relationships across the entire buying committee.

Learn more

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How to target developers using Activeloop Deep Lake

How to build your target account list?

Start by building your Ideal Customer Profile (ICP) universe using technology signals as a foundation. Companies using

Activeloop Deep Lake

often share similar technical maturity and infrastructure needs, making them prime candidates for developer-focused solutions. Learn our complete framework for building DevTool ICP account lists to maximize your targeting precision.

Customize this data by filtering for geography, industry, company size, revenue, technology usage, job positions and more. Our platform provides technology intelligence at both company and individual levels—categorized into developers/practitioners and economic buyers within those organizations. This dual-layer approach enables precise targeting whether you're running ABM campaigns at the account level or personalized outreach to specific contacts.

Download your refined lists in Excel or CSV format, sync directly to your CRM (HubSpot, Salesforce), or use our APIs to send data to your warehouse. For individual-level targeting, explore our

Companies using

database for direct practitioner and buyer intelligence.

How to get alerted when new developers are working on Activeloop Deep Lake technology?

Set up automated alerts to capture companies as they adopt

Activeloop Deep Lake

in real-time.

This gives your sales team first-mover advantage when prospects are actively evaluating and implementing new solutions—the optimal time for outreach.

Configure alerts based on your specific ICP criteria: get notified when companies in your target geography, industry, or size range start using your target technology. Alerts are delivered directly to your inbox with complete company and contact intelligence, enabling immediate, contextual outreach while the technology adoption signal is fresh.

How to sync this data with my CRM or sales stack?

Export technology user data seamlessly into your existing sales and marketing infrastructure. Direct CRM integrations with HubSpot and Salesforce automatically sync company and contact records with technology intelligence, enriching your existing database.

Use our API endpoints to send

Activeloop Deep Lake

user data directly to your data warehouse, enabling advanced segmentation and analytics across your entire revenue stack. This approach works particularly well for companies running sophisticated ABM programs or complex lead scoring models.

The targeting strategy differs significantly between contact-level outreach and account-based campaigns. For individual targeting, focus on practitioners who directly use

Activeloop Deep Lake

with personalized technical messaging. For ABM approaches, target economic buyers at companies using

Activeloop Deep Lake

with broader business value propositions and multi-threading strategies.

Frequently Asked Questions (FAQ)

What is Activeloop Deep Lake?

Activeloop Deep Lake is a cutting-edge technology that falls under the category of Machine Learning Data Infrastructure. It serves as a specialized data lake solution designed specifically for machine learning workflows, enabling organizations to efficiently store, version, and stream unstructured data such as images, videos, audio files, and 3D point clouds. Deep Lake addresses the critical challenges data scientists and ML engineers face when working with large-scale datasets by providing a seamless interface between data storage and ML frameworks.

Technically, Deep Lake implements a unique architecture that combines the benefits of data lakes with version control systems like Git. It stores data in a format optimized for ML workloads, using techniques like tensor-based storage, data streaming, and lazy loading to minimize memory usage while maximizing throughput. The platform integrates natively with popular ML frameworks including PyTorch, TensorFlow, and JAX, allowing data to be loaded directly into training pipelines without complex ETL processes. Deep Lake's version control capabilities enable teams to track dataset changes, create branches for experimentation, and maintain reproducibility across ML projects.

Deep Lake has gained significant adoption in the AI and machine learning community, particularly among organizations working with computer vision, natural language processing, and multimodal AI applications. Its ability to handle both structured and unstructured data at scale makes it valuable for teams building sophisticated ML models that require diverse training data. As the field of AI continues to advance, technologies like Deep Lake play an increasingly critical role in the ML infrastructure stack by bridging the gap between raw data storage and the specialized needs of modern machine learning workflows.

What is the source of this data?

We aggregate developer & company technographics intelligence from multiple proprietary and partner sources. Our platform monitors job postings across millions of companies—tracking listings on career sites, job boards, and recruitment platforms to identify technology adoption patterns and internal tool usage. This hiring signal data reveals what technologies organizations are actively investing in.

Beyond job data, Reo.Dev maintains a proprietary database of 30+ million developers and tracks activity across public GitHub repositories to capture real-time technology usage signals.

We supplement this with GDPR-compliant datasets from trusted data broker partners and visitor intelligence platforms, creating a comprehensive view of both company-level tech stacks and individual developer behaviors.

This multi-source approach ensures you're working with the most accurate, up-to-date company technographics & developer intelligence available.

How often is the data updated?

Our platform refreshes data daily, giving you access to the latest developer and technology intelligence. This continuous update cycle ensures your go-to-market teams are working with current information that reflects real-time market movements, emerging technology adoption patterns, and fresh hiring signals from across the industry.

How many developers use

Activeloop Deep Lake
?
As of now, we have data on
8,914
developers that use
Activeloop Deep Lake
.

How to find developers that use

Activeloop Deep Lake
?

Visit reo.dev and use Reo.Dev's audience builder to search for developers using your desired technology—our platform analyzes job postings, GitHub repositories, and proprietary developer data to identify the  technology stack for any given organization. Book a demo with us today to get started.

How to get an updated list of developers that use

Activeloop Deep Lake
?

Reo.Dev provides real-time access to companies using your desired technology of choice and thousands of other developer technologies. Our platform continuously tracks technology adoption signals from job postings, GitHub activity, and proprietary developer data to give you the most current view of which developers & organizations are actively using the technologies in their tech stack/developer profile. Simply search for your desired technology within our audience builder to generate a targeted list of developers—complete with their current company, seniority, years of experience, and tech stack intelligence at an account level. Book a demo with us today to get access to the latest data.