Forward Deployed Engineer
for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive info, not invent. Remove boilerplate etc.
We need to include sections only when posting has real specific content. So we need to extract content.
Let's parse:
About Actively AI ... (company description) - not needed as section? Possibly could be part of About the role? But the instruction says use headings for section headings like About the role, Responsibilities, etc. The company description is not part of those headings; maybe we can include it as a
before any heading? The instruction: "Output ONLY the HTML fragment — no /, no markdown code fences." It didn't forbid extra paragraphs before headings. But they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions.
Thus we can include a brief intro paragraph about the company (About Actively AI) as a
before any heading. Then headings for sections that have content.
We have:
About the Role: We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product.
That's prose for About the role.
What You'll Do (Responsibilities):
- Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results.
- Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion.
- Stay close to customers post-onboarding, continuing to iterate on performance and adoption.
- Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes.
- Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability.
- Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback.
What a Day Could Look Like:
- Help a new customer wire up their Salesforce data and translate custom fields into our schema.
- Jump into SQL or Python to troubleshoot data issues or assess model predictions.
- Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot.
- Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning.
- Write internal notes and product suggestions based on frontline learnings.
These are duties; we can put under Responsibilities as a list.
Who You Are (Qualifications/Skills):
- Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works.
- Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products.
- High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress.
- Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value.
- Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started.
- Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty.
Bonus Points:
- 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder.
- Experience working with enterprise customers or managing technical delivery.
- Background in a high-growth startup or elite tech company environment.
These can be under Requirements or Qualifications. We need to decide sections. Perhaps we can have Requirements (mandatory) and Qualifications (preferred). But the instruction says prefer a few substantive sections over many thin ones. Could combine Requirements and Qualifications into one section? But they are distinct headings allowed. However we should not create thin sections. The "Who You Are" is substantive; "Bonus Points" also substantive but could be part of same section as "Requirements". Might be better to have a single section "Requirements" that includes both mandatory and bonus points. But we also have "Skills" maybe separate? The "Who You Are" includes skill statements. Could treat as Skills section. But we need to avoid duplication.
Let's think: The posting includes "Who You Are" which describes traits and skills. That could be both Requirements and Skills. The "Bonus Points" are additional qualifications.
We could have sections: About the role, Responsibilities, Requirements (including Who You Are and Bonus Points), Benefits. That's three substantive sections. Avoid Skills, Qualifications, Pay, Schedule.
But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It doesn't forbid omitting some. So we can output only those we have content for.
Thus we will output:
About Actively AI ... (company description)
About the role
We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product.
Responsibilities
- Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results.
- Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion.
- Stay close to customers post-onboarding, continuing to iterate on performance and adoption.
- Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes.
- Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability.
- Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback.
- Help a new customer wire up their Salesforce data and translate custom fields into our schema.
- Jump into SQL or Python to troubleshoot data issues or assess model predictions.
- Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot.
- Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning.
- Write internal notes and product suggestions based on frontline learnings.
Requirements
- Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works.
- Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products.
- High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress.
- Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value.
- Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started.
- Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty.
- 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder.
- Experience working with enterprise customers or managing technical delivery.
- Background in a high-growth startup or elite tech company environment.
Benefits
- Competitive Early-Stage Equity
- Health, Dental, Vision Coverage
- Unlimited PTO + Recharge Days
- Catered Lunch on Tuesday & Friday, Dinners every day!
- Fully Stocked Kitchen
- Cutting-Edge Tech & Tools
- Annual Off-sites & Monthly Events
- Commuter Benefits
- Cozy Office in NYC
We need to ensure we used the exact emojis? The benefits list had emojis; we can keep them or strip? The instruction says preserve ALL substantive information; emojis are part of the benefit description. We can keep them as text. But we should maybe keep them as they appear. However the benefit list in the posting includes emojis before each item: 🚀 Competitive Early-Stage Equity, ⚕ Health, Dental, Vision Coverage, etc. We'll keep them.
We also need to ensure we didn't include duplicated lines. The benefits appear twice in the posting (once near the top and again later). We should only include once.
We also need to ensure we didn't include boilerplate like "About Actively" repeated. We included a paragraph about Actively AI at start. That's fine.
Check for any other substantive content: There's also "About Actively" paragraph after the company description: "Actively is building superintelligence for GTM teams..." That's also substantive about the company. Should we include that? It's not under a heading but it's substantive. We could add another after the first paragraph. The instruction: preserve ALL substantive information. So we should include that as well.
Thus we will have two paragraphs: first about Actively AI (the longer description), second about Actively (the second paragraph). Let's capture both.
First paragraph: "About Actively AI Actively AI is defining a new category: Intelligence-Led Revenue. Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them. Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify
for prose. We need to preserve all substantive info, not invent. Remove boilerplate etc. We need to include sections only when posting has real specific content. So we need to extract content. Let's parse: About Actively AI ... (company description) - not needed as section? Possibly could be part of About the role? But the instruction says use headings for section headings like About the role, Responsibilities, etc. The company description is not part of those headings; maybe we can include it as a
before any heading? The instruction: "Output ONLY the HTML fragment — no /, no markdown code fences." It didn't forbid extra paragraphs before headings. But they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions.
Thus we can include a brief intro paragraph about the company (About Actively AI) as a
before any heading. Then headings for sections that have content.
We have:
About the Role: We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product.
That's prose for About the role.
What You'll Do (Responsibilities):
- Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results.
- Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion.
- Stay close to customers post-onboarding, continuing to iterate on performance and adoption.
- Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes.
- Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability.
- Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback.
What a Day Could Look Like:
- Help a new customer wire up their Salesforce data and translate custom fields into our schema.
- Jump into SQL or Python to troubleshoot data issues or assess model predictions.
- Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot.
- Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning.
- Write internal notes and product suggestions based on frontline learnings.
These are duties; we can put under Responsibilities as a list.
Who You Are (Qualifications/Skills):
- Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works.
- Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products.
- High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress.
- Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value.
- Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started.
- Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty.
Bonus Points:
- 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder.
- Experience working with enterprise customers or managing technical delivery.
- Background in a high-growth startup or elite tech company environment.
These can be under Requirements or Qualifications. We need to decide sections. Perhaps we can have Requirements (mandatory) and Qualifications (preferred). But the instruction says prefer a few substantive sections over many thin ones. Could combine Requirements and Qualifications into one section? But they are distinct headings allowed. However we should not create thin sections. The "Who You Are" is substantive; "Bonus Points" also substantive but could be part of same section as "Requirements". Might be better to have a single section "Requirements" that includes both mandatory and bonus points. But we also have "Skills" maybe separate? The "Who You Are" includes skill statements. Could treat as Skills section. But we need to avoid duplication.
Let's think: The posting includes "Who You Are" which describes traits and skills. That could be both Requirements and Skills. The "Bonus Points" are additional qualifications.
We could have sections: About the role, Responsibilities, Requirements (including Who You Are and Bonus Points), Benefits. That's three substantive sections. Avoid Skills, Qualifications, Pay, Schedule.
But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It doesn't forbid omitting some. So we can output only those we have content for.
Thus we will output:
About Actively AI ... (company description)
About the role
We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product.
Responsibilities
- Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results.
- Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion.
- Stay close to customers post-onboarding, continuing to iterate on performance and adoption.
- Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes.
- Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability.
- Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback.
- Help a new customer wire up their Salesforce data and translate custom fields into our schema.
- Jump into SQL or Python to troubleshoot data issues or assess model predictions.
- Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot.
- Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning.
- Write internal notes and product suggestions based on frontline learnings.
Requirements
- Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works.
- Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products.
- High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress.
- Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value.
- Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started.
- Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty.
- 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder.
- Experience working with enterprise customers or managing technical delivery.
- Background in a high-growth startup or elite tech company environment.
Benefits
- Competitive Early-Stage Equity
- Health, Dental, Vision Coverage
- Unlimited PTO + Recharge Days
- Catered Lunch on Tuesday & Friday, Dinners every day!
- Fully Stocked Kitchen
- Cutting-Edge Tech & Tools
- Annual Off-sites & Monthly Events
- Commuter Benefits
- Cozy Office in NYC
We need to ensure we used the exact emojis? The benefits list had emojis; we can keep them or strip? The instruction says preserve ALL substantive information; emojis are part of the benefit description. We can keep them as text. But we should maybe keep them as they appear. However the benefit list in the posting includes emojis before each item: 🚀 Competitive Early-Stage Equity, ⚕ Health, Dental, Vision Coverage, etc. We'll keep them.
We also need to ensure we didn't include duplicated lines. The benefits appear twice in the posting (once near the top and again later). We should only include once.
We also need to ensure we didn't include boilerplate like "About Actively" repeated. We included a paragraph about Actively AI at start. That's fine.
Check for any other substantive content: There's also "About Actively" paragraph after the company description: "Actively is building superintelligence for GTM teams..." That's also substantive about the company. Should we include that? It's not under a heading but it's substantive. We could add another after the first paragraph. The instruction: preserve ALL substantive information. So we should include that as well.
Thus we will have two paragraphs: first about Actively AI (the longer description), second about Actively (the second paragraph). Let's capture both.
First paragraph: "About Actively AI Actively AI is defining a new category: Intelligence-Led Revenue. Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them. Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Thus we can include a brief intro paragraph about the company (About Actively AI) as a
before any heading. Then headings for sections that have content. We have: About the Role: We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product. That's prose for About the role. What You'll Do (Responsibilities): - Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results. - Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion. - Stay close to customers post-onboarding, continuing to iterate on performance and adoption. - Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes. - Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability. - Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback. What a Day Could Look Like: - Help a new customer wire up their Salesforce data and translate custom fields into our schema. - Jump into SQL or Python to troubleshoot data issues or assess model predictions. - Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot. - Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning. - Write internal notes and product suggestions based on frontline learnings. These are duties; we can put under Responsibilities as a list. Who You Are (Qualifications/Skills): - Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works. - Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products. - High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress. - Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value. - Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started. - Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty. Bonus Points: - 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder. - Experience working with enterprise customers or managing technical delivery. - Background in a high-growth startup or elite tech company environment. These can be under Requirements or Qualifications. We need to decide sections. Perhaps we can have Requirements (mandatory) and Qualifications (preferred). But the instruction says prefer a few substantive sections over many thin ones. Could combine Requirements and Qualifications into one section? But they are distinct headings allowed. However we should not create thin sections. The "Who You Are" is substantive; "Bonus Points" also substantive but could be part of same section as "Requirements". Might be better to have a single section "Requirements" that includes both mandatory and bonus points. But we also have "Skills" maybe separate? The "Who You Are" includes skill statements. Could treat as Skills section. But we need to avoid duplication. Let's think: The posting includes "Who You Are" which describes traits and skills. That could be both Requirements and Skills. The "Bonus Points" are additional qualifications. We could have sections: About the role, Responsibilities, Requirements (including Who You Are and Bonus Points), Benefits. That's three substantive sections. Avoid Skills, Qualifications, Pay, Schedule. But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It doesn't forbid omitting some. So we can output only those we have content for. Thus we will output:
About Actively AI ... (company description)
About the role
We’re looking for high-agency, technically sharp Forward Deployed Engineers to work hand-in-hand with our customers and own their success from day one. This is a deeply cross-functional role that blends engineering, data science, product thinking, and customer interaction — all in service of one goal: making Actively deliver outsized value for every single customer. You’ll run customer pilots, handle complex data onboarding, work closely with LLMs and model performance, and then feed your learnings directly into the product and tooling to make success more scalable and repeatable over time. You’ll be directly unblocking revenue growth and shaping the next evolution of our product.
Responsibilities
- Own the full lifecycle of onboarding new customers — from integrating with Salesforce to configuring our system to deliver real results.
- Run multi-week pilots, helping prove out ROI and guiding customers toward full conversion.
- Stay close to customers post-onboarding, continuing to iterate on performance and adoption.
- Analyze data and model performance, dig into GTM workflows, and work with engineering to improve outcomes.
- Build tooling, workflows, and processes that reduce future onboarding time and increase self-serve capability.
- Partner directly with founders and product/engineering teams to drive roadmap decisions based on field feedback.
- Help a new customer wire up their Salesforce data and translate custom fields into our schema.
- Jump into SQL or Python to troubleshoot data issues or assess model predictions.
- Coordinate with reps, managers, and ops leaders to adjust messaging or outputs mid-pilot.
- Debrief a customer pilot with engineering to identify opportunities for tooling or LLM tuning.
- Write internal notes and product suggestions based on frontline learnings.
Requirements
- Detail-Oriented Translator – You’re great at understanding requests from different stakeholders and translating them into code or logic that works.
- Engineer with Depth – Strong Python and SQL, familiarity with ML fundamentals, and interest (or experience) in LLM-based products.
- High Ownership Operator – You act like a founder. You do what it takes to make customers successful and unblock progress.
- Customer Obsessed – You love talking to users, understanding pain, and finding creative ways to deliver value.
- Fast Learner with Steep Trajectory – You’ve already accomplished a lot, and you’re just getting started.
- Hands-On Builder – You’ve been in roles that required delivery, not just theory — and you love getting your hands dirty.
- 3+ years in roles like Solutions Engineer, Forward Deployed Engineer, Data Scientist, or startup founder.
- Experience working with enterprise customers or managing technical delivery.
- Background in a high-growth startup or elite tech company environment.
Benefits
- Competitive Early-Stage Equity
- Health, Dental, Vision Coverage
- Unlimited PTO + Recharge Days
- Catered Lunch on Tuesday & Friday, Dinners every day!
- Fully Stocked Kitchen
- Cutting-Edge Tech & Tools
- Annual Off-sites & Monthly Events
- Commuter Benefits
- Cozy Office in NYC
after the first paragraph. The instruction: preserve ALL substantive information. So we should include that as well. Thus we will have two paragraphs: first about Actively AI (the longer description), second about Actively (the second paragraph). Let's capture both. First paragraph: "About Actively AI Actively AI is defining a new category: Intelligence-Led Revenue. Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them. Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify