Actively is building superintelligence for GTM teams that tells sales reps what to do every day (which prospects to contact, when, and what to say) to maximize revenue. We train custom reasoning agents to think like a company’s best sales reps, and then use them to evaluate millions of datapoint about every single prospect account in the company’s TAM to identify the optimal actions to take.
Our customers include some of the fastest-growing companies in the world (Ramp, Verkada, Justworks, Ironclad, etc.), for whom we’ve driven hundreds of millions of dollars in added revenue in the past year. We’re growing incredibly quickly ourselves (10x revenue in the past <year), have raised tens of millions of dollars from top investors, work in person in NYC, and are looking for exceptionally talented folks to join the team and own large parts of what are building - across every role but we'd be especially excited to talk if you're excited to build AI agents!
Feel free to reach out to me directly (I'm one of the founders): mihir [at] actively.ai :)
Actively | Software Engineers (LLM/backend/data infra, very product-y) & Forward Deployed Engineers (great data/SQL chops, great at understanding customer problems, and strong engineer in general to automate work using LLMs/internal tools) | Full-time | NYC | Onsite
I'm one of the founders of Actively. We're building best-in-class machine learning and LLM-powered scoring for outbound sales teams. Our first product lives in Salesforce, ingests data from a variety of sources (firmographics, intent signals, marketing interactions, and prior engagement, including unstructured call transcripts, email chains, and notes), and combines LLMs with structured models to tell sales reps who they should reach out to each day (and why) to be laser-focused.
Our paying customers are fast-growing unicorns you've definitely heard of with large outbound sales motions, where the reps use us to target their outbound every day. Our thesis is that scoring (a known/"need to have" part of the stack -- but able to be done 10x better with LLMs because you can incorporate the goldmine of unstructured engagement data) is the right wedge into building the intelligence-driven outbound sales stack of the future that _actually works_ for large companies that need to generate millions of dollars in pipeline each quarter. Unlike most other companies in the space, we are explicitly building for companies of this scale and have them using our product today "in production." We have a 100% close rate from pilot → paying customer and want to scale our customer base aggressively this year.
We started with the vision of bringing AI to every business team, and have since narrowed down to focus on outbound sales teams who have a bunch of attractive properties (there are a _ton_ of them out there, they all use the same stack, the data is consolidated in one place, lift is measurable and directly touches the top line, etc.).
We're a small team from Scale/Waymo/Google/etc. (including some very senior folks) working in-person in NYC and looking to bring on a couple more engineers as we start scaling our customer base, in both product engineering & forward deployed engineering roles. If you have at least a few years of experience, are highly entrepreneurial / excited to work closely with customers + dig in across the business, and are excited to work on some combination of LLMs/data infra/across the stack, we would love to chat: mihir [at] actively.ai!
Actively | Software Engineers (LLM/backend/data infra, very product-y) & Solutions Engineers (great data/SQL chops, great at understanding customer problems, and strong engineer in general to automate work using LLMs/internal tools) | Full-time | NYC | Onsite
I'm one of the founders of Actively. We're building best-in-class machine learning and LLM-powered scoring for outbound sales teams. Our first product lives in Salesforce, ingests data from a variety of sources (firmographics, intent signals, marketing interactions, and prior engagement, including unstructured call transcripts, email chains, and notes), and combines LLMs with structured models to tell sales reps who they should reach out to each day (and why) to be laser-focused.
Our paying customers are fast-growing unicorns you've definitely heard of with large outbound sales motions, where the reps use us to target their outbound every day. Our thesis is that scoring (a known/"need to have" part of the stack -- but able to be done 10x better with LLMs because you can incorporate the goldmine of unstructured engagement data) is the right wedge into building the intelligence-driven outbound sales stack of the future that _actually works_ for large companies that need to generate millions of dollars in pipeline each quarter. Unlike most other companies in the space, we are explicitly building for companies of this scale and have them using our product today "in production." We have a 100% close rate from pilot → paying customer and want to scale our customer base aggressively this year.
We started with the vision of bringing AI to every business team, and have since narrowed down to focus on outbound sales teams who have a bunch of attractive properties (there are a _ton_ of them out there, they all use the same stack, the data is consolidated in one place, lift is measurable and directly touches the top line, etc.).
We're a team of 6 working in-person in NYC with a team from Scale/Waymo/Google/etc. (including some very senior folks) and looking to bring on a couple more engineers as we start scaling our customer base, in both product & solutions engineering roles. If you have at least a few years of experience, are highly entrepreneurial / excited to work closely with customers + dig in across the business, and are excited to work on some combination of LLMs/data infra/across the stack, we would love to chat: mihir [at] actively.ai!
Actively | Software Engineers (some combination of LLMs/backend/data infra and general engineering across the stack) | Full-time | NYC | Onsite
I'm one of the founders of Actively. We're building best-in-class machine learning and LLM-powered scoring for outbound sales teams. Our first product lives in Salesforce, ingests data from a variety of sources (firmographics, intent signals, marketing interactions, and prior engagement, including unstructured call transcripts, email chains, and notes), and combines LLMs with structured models to tell sales reps who they should reach out to each day (and why) to be laser-focused.
Our paying customers are fast-growing unicorns you've definitely heard of with large outbound sales motions, where the reps use us to target their outbound every day. Our thesis is that scoring (a known/"need to have" part of the stack -- but able to be done 10x better with LLMs because you can incorporate the goldmine of unstructured engagement data) is the right wedge into building the intelligence-driven outbound sales stack of the future that _actually works_ for large companies that need to generate millions of dollars in pipeline each quarter. Unlike most other companies in the space, we are explicitly building for companies of this scale and have them using our product today "in production."
We started with the vision of bringing AI to every business team, and have since narrowed down to focus on outbound sales teams who have a bunch of attractive properties (there are a _ton_ of them out there, they all use the same stack, the data is consolidated in one place, lift is measurable and directly touches the top line, etc.).
We're a team of 6 working in-person in NYC with a team from Scale/Waymo/Google/etc. (including some very senior folks) and looking to bring on another engineer as we start scaling our customer base. If you have at least a few years of experience, are highly entrepreneurial / excited to work closely with customers + dig in across the business, and are excited to work on some combination of LLMs/data infra/across the stack, we would love to chat: mihir [at] actively.ai!
Actively | Software Engineers (some combination of LLMs/backend/data infra and general engineering across the stack) | Full-time | NYC | Onsite
I'm one of the founders of Actively. We're building best-in-class machine learning and LLM-powered scoring for outbound sales teams. Our first product lives in Salesforce, ingests data from a variety of sources (firmographics, intent signals, marketing interactions, and prior engagement, including unstructured call transcripts, email chains, and notes), and combines LLMs with structured models to tell sales reps who they should reach out to each day (and why) to be laser-focused.
Our paying customers are fast-growing unicorns you've definitely heard of with large outbound sales motions, where the reps use us to target their outbound every day. Our thesis is that scoring (a known/"need to have" part of the stack -- but able to be done 10x better with LLMs because you can incorporate the goldmine of unstructured engagement data) is the right wedge into building the intelligence-driven outbound sales stack of the future that _actually works_ for large companies that need to generate millions of dollars in pipeline each quarter. Unlike most other companies in the space, we are explicitly building for companies of this scale and have them using our product today "in production."
We started with the vision of bringing AI to every business team, and have since narrowed down to focus on outbound sales teams who have a bunch of attractive properties (there are a _ton_ of them out there, they all use the same stack, the data is consolidated in one place, lift is measurable and directly touches the top line, etc.).
We're a team of 6 working in-person in NYC with a team from Scale/Waymo/Google/etc. (including some very senior folks) and looking to bring on another engineer as we start scaling our customer base. If you have at least a few years of experience, are highly entrepreneurial / excited to work closely with customers + dig in across the business, and are excited to work on some combination of LLMs/data infra/across the stack, we would love to chat: mihir [at] actively.ai!
Actively | Software Engineers (some combination of LLMs/backend/data infra and general engineering across the stack) | Full-time | NYC | Onsite
I'm one of the founders of Actively. We're building best-in-class machine learning and LLM-powered scoring for outbound sales teams. Our first product lives in Salesforce, ingests data from a variety of sources (firmographics, intent signals, marketing interactions, and prior engagement, including unstructured call transcripts, email chains, and notes), and combines LLMs with structured models to tell sales reps who they should reach out to each day (and why) to be laser-focused.
Our paying customers are fast-growing unicorns you've definitely heard of with large outbound sales motions, where the reps use us to target their outbound every day. Our thesis is that scoring (a known/"need to have" part of the stack -- but able to be done 10x better with LLMs because you can incorporate the goldmine of unstructured engagement data) is the right wedge into building the intelligence-driven outbound sales stack of the future that _actually works_ for large companies that need to generate millions of dollars in pipeline each quarter. Unlike most other companies in the space, we are explicitly building for companies of this scale and have them using our product today "in production."
We started with the vision of bringing AI to every business team, and have since narrowed down to focus on outbound sales teams who have a bunch of attractive properties (there are a _ton_ of them out there, they all use the same stack, the data is consolidated in one place, lift is measurable and directly touches the top line, etc.).
We're a team of 6 working in-person in NYC with a team from Scale/Waymo/Google/etc. (including some very senior folks) and looking to bring on another engineer as we start scaling our customer base. If you have at least a few years of experience, are highly entrepreneurial / excited to work closely with customers + dig in across the business, and are excited to work on some combination of LLMs/data infra/across the stack, we would love to chat: mihir [at] actively.ai!
I'm one of the founders of Actively — our vision is to turn every knowledge worker into a data scientist by building interactive ML tools that combine human expertise with machine intelligence. Here's [more on us](https://actively-ai.notion.site/About-Actively-9dabd208d3a54...) + a demo of some of the core tech if you're curious :)
We're based in NYC and are well-funded by top investors; our (paying!) customers are analytical business teams (think growth, sales, marketing, etc.) at Series C through IPO companies who have tons of questions to answer but very limited access to data scientists.
We think of our first product as a "metrics optimization platform" where business users connect to their data, plug in any KPI they care about as an aggregation over that data, and define their levers, and then we continuously optimize that business metric (conversion, churn, upsell, activation, etc.) -- incredibly quickly and without the need for a data scientist.
Our thesis is that the two barriers to widespread adoption of AI/ML among business teams are (1) lack of clarity around ROI upfront and (2) the large amount of effort/time required to implement a solution (hire a data scientist, build a bunch of data infra, have them work for a few months, etc.). We solve these by building the right abstractions and human-in-the-loop interfaces to make it incredibly quick for business teams to unlock value from their data -- so quick that we can plug into their data and show them the ROI within a few minutes!
We're fairly small (our current team comes from Stanford/Google/Waymo/Scale AI/Microsoft) but are growing the team based on customer demand and looking for entrepreneurial, experienced product and infrastructure engineers who are also excited to work closely with customers + dig in on product & across the business! I'd love to chat: mihir [at] actively.ai!
Keywords: AI, ML, machine learning, active learning, explainable AI, causal inference, data science, full-stack, product engineering, data infrastructure
Actively | Software Engineers (ML & full-stack) | Full-time | NYC | Onsite
I'm one of the founders of Actively — our vision is to turn every knowledge worker into a data scientist by building interactive ML tools that combine human expertise with machine intelligence.
We're based in NYC and are well-funded by top investors; our (paying!) customers are analytical business teams (think growth, sales, marketing, etc.) at Series C through IPO companies who have tons of questions to answer but very limited access to data scientists. We think of our first product as a "metrics optimization platform" where business users plug in their KPI as an aggregation over their data and define their levers and we continuously optimize that business metric.
We're super small (our current team comes from Stanford/Google/Waymo/Microsoft) but are growing the team "actively" (!!) based on customer demand and looking for founding ML and product engineers (ideally senior and entrepreneurial and excited to dig in on product + across the business!) [on the ML side, any of ML research/quant experience/experience with explainability or causal inference are huge pluses; on the full-stack side, side projects and/or experience building real products with React and Typescript is great, bonus for data or analytics-related products]. I'd love to chat: mihir [at] actively.ai!
Keywords: AI, ML, machine learning, active learning, explainable AI, causal inference, data science, quant
I'm one of the founders of Actively — our vision is to turn every knowledge worker into a data scientist by building interactive ML tools that combine human expertise with machine intelligence.
We're based in NYC and are well-funded by top investors; our (paying!) customers are analytical business teams (think growth, sales, marketing, etc.) at Series C through IPO companies who have tons of questions to answer but very limited access to data scientists. We think of our first product as a "metrics optimization platform" where business users plug in their KPI as an aggregation over their data and define their levers and we continuously optimize that business metric.
We're super small (our current team comes from Stanford/Google/Waymo/Microsoft) but are growing the team "actively" (!!) based on customer demand and looking for founding ML and product engineers (ideally senior and entrepreneurial and excited to dig in on product + across the business!) [on the ML side, any of ML research/quant experience/experience with explainability or causal inference are huge pluses; on the full-stack side, side projects and/or experience building real products with React and Typescript is great, bonus for data or analytics-related products]. I'd love to chat: mihir [at] actively.ai!
Keywords: AI, ML, machine learning, active learning, explainable AI, causal inference, data science, quant
Don't think it's exactly what you're talking about (I'm sure there are other works much closer to what you have in mind, just can't recall off the top of my head) — but you might find PoseNet (https://www.cv-foundation.org/openaccess/content_iccv_2015/p...) interesting. Not explicitly 3D, but estimates where in a large-scale scene a picture was taken using an end-to-end convolutional network.
With that said, I think there's still a ton of merit in classical geometric approaches like ICP — there's a real, geometric basis to why they work. Convolutional networks can demonstrate some pretty amazing results, but they're still mostly "black boxes" to us, and a consequence of this is that it's hard to understand why they work and predict when they'll fail. This blog post (by the PoseNet author, actually) articulates the viewpoint well: https://alexgkendall.com/computer_vision/have_we_forgotten_a.... One recent research direction that I personally find really fascinating is designing deep learning architectures around real geometric properties, e.g. as in Skydio's deep stereo work: https://arxiv.org/pdf/1703.04309.pdf
I've been playing around with a few and I'd recommend the Orbbec Astra and the Intel RealSense (the new D435 is what I've been using) as decent but cheap cameras if you want to get started! The Asus Xtion PRO LIVE is also quite good but since it's been discontinued it's pretty hard to find.
The Stereolabs ZED relies on stereo vision but produces a similar output as traditional RGB-D cameras, and I've heard good things about it as well!
I don't think that there's anything wrong with writing code itself, but perhaps apply it in a different context, e.g. robotics or computer vision, to actually make a contribution.
Actively is building superintelligence for GTM teams that tells sales reps what to do every day (which prospects to contact, when, and what to say) to maximize revenue. We train custom reasoning agents to think like a company’s best sales reps, and then use them to evaluate millions of datapoint about every single prospect account in the company’s TAM to identify the optimal actions to take.
Our customers include some of the fastest-growing companies in the world (Ramp, Verkada, Justworks, Ironclad, etc.), for whom we’ve driven hundreds of millions of dollars in added revenue in the past year. We’re growing incredibly quickly ourselves (10x revenue in the past <year), have raised tens of millions of dollars from top investors, work in person in NYC, and are looking for exceptionally talented folks to join the team and own large parts of what are building - across every role but we'd be especially excited to talk if you're excited to build AI agents!
Feel free to reach out to me directly (I'm one of the founders): mihir [at] actively.ai :)