Software Engineer - Applied AI
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to map content to those headings where appropriate.
We have:
- Company Overview: description of company, mission, culture. Could be "About the role"? Actually "About the role" is more about the job. But we can use "About the role" for a brief description of the role and company? The instruction: "Do NOT repeat the job title as a heading (the page already shows it)". So we can have sections like About the role (maybe includes company overview and job description summary). Responsibilities: from "How you’ll make an impact" bullet points. Requirements/Qualifications: from "What you’ll need to succeed". Skills: maybe list of technical skills? Could be part of Requirements. Benefits: from "Why Join Us". Pay and Schedule: not present, so omit.
We must not create sections with placeholder text. So only include sections where we have real content.
We need to use - for any list of duties/requirements/benefits, and
for prose.
We must preserve all substantive information; do not invent or summarize away detail. So we need to keep all details from those sections.
Let's extract:
Company Overview paragraph: "At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to create flexible financing solutions that help people move forward. We're proud of our inclusive, supportive culture, built on empowering our customers, partners, and team members alike. When our people thrive, so does our innovation. If you're looking to make an impact and grow with a team that values you, come join us!"
Job Description paragraph: "Snap Finance is looking for an entry-level Software Engineer to join our Applied AI team. You'll build the production applications and infrastructure that our Machine Learning and Analytics teams rely on, the systems behind credit decisioning, pricing, and risk. This is a great fit for a recent Computer Science or Math graduate who wants to write real software, work alongside experienced ML and analytics engineers, and grow quickly in a company where engineering directly drives the business."
How you’ll make an impact bullet points (list):
- Build real applications from day one
- Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow.
- Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production.
- Help bring machine learning into production
- Work with our ML and Analytics teams to build the services and infrastructure that their models run on.
- Learn how production ML systems are built and maintained at scale, without needing to build the models yourself.
- Learn to connect engineering to business impact
- Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving.
- Practice explaining technical decisions clearly to non-technical teammates.
- Build with modern tools
- Use coding agents and other AI-assisted development tools to move faster from idea to shipped code.
- Stay curious about new tools in software engineering and bring what you learn back to the team.
What you’ll need to succeed bullet points:
- B.S. in Computer Science, Mathematics, or a related quantitative field (0-2 years of experience).
- Solid programming fundamentals in Python; familiarity with SQL.
- Experience using coding agents or other AI-assisted development tools.
- Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job.
- Exposure to ML concepts (coursework or personal projects) is a plus.
Why Join Us bullet points (benefits):
- Generous paid time off
- Competitive medical, dental & vision coverage
- 401K with company match for US
- Company-paid life insurance
- Company-paid short-term and long-term disability
- Access to mental health and wellness resources
- Company-paid volunteer time to do good in your community
- Legal coverage and other supplemental options
- A value-based culture where growth opportunities are endless
We should not include EEO etc.
Now map to headings:
- About the role: Could combine Company Overview + Job Description? Or we could have "About the role" for job description summary, and maybe "About the company" not in list. But the allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can use About the role for a brief overview of the role and company. Responsibilities for impact bullets. Requirements for education/experience. Qualifications maybe for technical skills? Skills maybe for specific skills like Python, SQL, coding agents. Benefits for Why Join Us.
We need to avoid duplication. Let's decide:
About the role: include Company Overview and Job Description paragraphs (two paragraphs). That seems appropriate.
Responsibilities: the "How you’ll make an impact" bullets.
Requirements: maybe the education/experience bullet (B.S. ... 0-2 years). Could also include "Solid programming fundamentals in Python; familiarity with SQL." but that's more skill. We'll see.
Qualifications: maybe the "Experience using coding agents or other AI-assisted development tools." and "Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job." and "Exposure to ML concepts (coursework or personal projects) is a plus."
Skills: maybe list of specific technical skills: Python, SQL, coding agents/AI-assisted tools.
But we need to ensure we don't create thin sections. Let's see if we have enough content for each.
We have:
- About the role: two paragraphs (good)
- Responsibilities: many bullets (good)
- Requirements: education/experience (one bullet) maybe also include "0-2 years of experience". That's okay.
- Qualifications: problem-solving, willingness to learn, ML exposure plus (maybe 2-3 bullets)
- Skills: Python, SQL, experience with coding agents/AI-assisted tools (maybe 3 bullets)
- Benefits: many bullets (good)
We should not include Pay or Schedule.
Now we need to output HTML fragment with
headings and
for prose,
- for lists.
We must not include any extra text outside HTML.
Let's craft:
About the role
At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to create flexible financing solutions that help people move forward. We're proud of our inclusive, supportive culture, built on empowering our customers, partners, and team members alike. When our people thrive, so does our innovation. If you're looking to make an impact and grow with a team that values you, come join us!
Snap Finance is looking for an entry-level Software Engineer to join our Applied AI team. You'll build the production applications and infrastructure that our Machine Learning and Analytics teams rely on, the systems behind credit decisioning, pricing, and risk. This is a great fit for a recent Computer Science or Math graduate who wants to write real software, work alongside experienced ML and analytics engineers, and grow quickly in a company where engineering directly drives the business.
Responsibilities
- Build real applications from day one
- Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow.
- Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production.
- Help bring machine learning into production
- Work with our ML and Analytics teams to build the services and infrastructure that their models run on.
- Learn how production ML systems are built and maintained at scale, without needing to build the models yourself.
- Learn to connect engineering to business impact
- Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving.
- Practice explaining technical decisions clearly to non-technical teammates.
- Build with modern tools
- Use coding agents and other AI-assisted development tools to move faster from idea to shipped code.
- Stay curious about new tools in software engineering and bring what you learn back to the team.
Requirements
- B.S. in Computer Science, Mathematics, or a related quantitative field (0-2 years of experience).
Qualifications
- Solid programming fundamentals in Python; familiarity with SQL.
- Experience using coding agents or other AI-assisted development tools.
- Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job.
- Exposure to ML concepts (coursework or personal projects) is a plus.
Skills
- Python programming
- SQL
- Coding agents / AI-assisted development tools
Benefits
- Generous paid time off
- Competitive medical, dental & vision coverage
- 401K with company match for US
- Company-paid life insurance
- Company-paid short-term and long-term disability
- Access to mental health and wellness resources
- Company-paid volunteer time to do good in your community
- Legal coverage and other supplemental options
- A value-based culture where growth opportunities are endless
Check that we didn't include any boilerplate. We omitted EEO lines, "More:", "Learn more by visiting our website", "California Residents..." etc. Good.
We used headings as required. No duplicate job title. No Pay/Schedule sections.
We used
for prose paragraphs. We used
- for lists.
We preserved all substantive information: company overview, job description, responsibilities bullets, requirements bullet (education), qualifications bullets (skills, experience, problem solving, ML exposure), skills bullets (Python, SQL, coding agents), benefits bullets.
Check if any detail missed: The responsibilities bullet "Build real applications from day one" is a short phrase; we kept it. The bullet "Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow." kept. "Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production." kept. "Help bring machine learning into production" kept. "Work with our ML and Analytics teams to build the services and infrastructure that their models run on." kept. "Learn how production ML systems are built and maintained at scale, without needing to build the models yourself." kept. "Learn to connect engineering to business impact" kept. "Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving." kept. "Practice explaining technical decisions clearly to non-technical teammates." kept. "Build with modern tools" kept. "Use coding agents and other AI-assisted development tools to move faster from idea to shipped code." kept. "Stay curious about new tools
for prose. We must preserve all substantive information; do not invent or summarize away detail. So we need to keep all details from those sections. Let's extract: Company Overview paragraph: "At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to create flexible financing solutions that help people move forward. We're proud of our inclusive, supportive culture, built on empowering our customers, partners, and team members alike. When our people thrive, so does our innovation. If you're looking to make an impact and grow with a team that values you, come join us!" Job Description paragraph: "Snap Finance is looking for an entry-level Software Engineer to join our Applied AI team. You'll build the production applications and infrastructure that our Machine Learning and Analytics teams rely on, the systems behind credit decisioning, pricing, and risk. This is a great fit for a recent Computer Science or Math graduate who wants to write real software, work alongside experienced ML and analytics engineers, and grow quickly in a company where engineering directly drives the business." How you’ll make an impact bullet points (list): - Build real applications from day one - Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow. - Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production. - Help bring machine learning into production - Work with our ML and Analytics teams to build the services and infrastructure that their models run on. - Learn how production ML systems are built and maintained at scale, without needing to build the models yourself. - Learn to connect engineering to business impact - Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving. - Practice explaining technical decisions clearly to non-technical teammates. - Build with modern tools - Use coding agents and other AI-assisted development tools to move faster from idea to shipped code. - Stay curious about new tools in software engineering and bring what you learn back to the team. What you’ll need to succeed bullet points: - B.S. in Computer Science, Mathematics, or a related quantitative field (0-2 years of experience). - Solid programming fundamentals in Python; familiarity with SQL. - Experience using coding agents or other AI-assisted development tools. - Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job. - Exposure to ML concepts (coursework or personal projects) is a plus. Why Join Us bullet points (benefits): - Generous paid time off - Competitive medical, dental & vision coverage - 401K with company match for US - Company-paid life insurance - Company-paid short-term and long-term disability - Access to mental health and wellness resources - Company-paid volunteer time to do good in your community - Legal coverage and other supplemental options - A value-based culture where growth opportunities are endless We should not include EEO etc. Now map to headings: - About the role: Could combine Company Overview + Job Description? Or we could have "About the role" for job description summary, and maybe "About the company" not in list. But the allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can use About the role for a brief overview of the role and company. Responsibilities for impact bullets. Requirements for education/experience. Qualifications maybe for technical skills? Skills maybe for specific skills like Python, SQL, coding agents. Benefits for Why Join Us. We need to avoid duplication. Let's decide: About the role: include Company Overview and Job Description paragraphs (two paragraphs). That seems appropriate. Responsibilities: the "How you’ll make an impact" bullets. Requirements: maybe the education/experience bullet (B.S. ... 0-2 years). Could also include "Solid programming fundamentals in Python; familiarity with SQL." but that's more skill. We'll see. Qualifications: maybe the "Experience using coding agents or other AI-assisted development tools." and "Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job." and "Exposure to ML concepts (coursework or personal projects) is a plus." Skills: maybe list of specific technical skills: Python, SQL, coding agents/AI-assisted tools. But we need to ensure we don't create thin sections. Let's see if we have enough content for each. We have: - About the role: two paragraphs (good) - Responsibilities: many bullets (good) - Requirements: education/experience (one bullet) maybe also include "0-2 years of experience". That's okay. - Qualifications: problem-solving, willingness to learn, ML exposure plus (maybe 2-3 bullets) - Skills: Python, SQL, experience with coding agents/AI-assisted tools (maybe 3 bullets) - Benefits: many bullets (good) We should not include Pay or Schedule. Now we need to output HTML fragment with
headings and
for prose,
- for lists.
We must not include any extra text outside HTML.
Let's craft:
About the role
At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we've used data, machine learning, and a more human approach to create flexible financing solutions that help people move forward. We're proud of our inclusive, supportive culture, built on empowering our customers, partners, and team members alike. When our people thrive, so does our innovation. If you're looking to make an impact and grow with a team that values you, come join us!
Snap Finance is looking for an entry-level Software Engineer to join our Applied AI team. You'll build the production applications and infrastructure that our Machine Learning and Analytics teams rely on, the systems behind credit decisioning, pricing, and risk. This is a great fit for a recent Computer Science or Math graduate who wants to write real software, work alongside experienced ML and analytics engineers, and grow quickly in a company where engineering directly drives the business.
Responsibilities
- Build real applications from day one
- Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow.
- Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production.
- Help bring machine learning into production
- Work with our ML and Analytics teams to build the services and infrastructure that their models run on.
- Learn how production ML systems are built and maintained at scale, without needing to build the models yourself.
- Learn to connect engineering to business impact
- Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving.
- Practice explaining technical decisions clearly to non-technical teammates.
- Build with modern tools
- Use coding agents and other AI-assisted development tools to move faster from idea to shipped code.
- Stay curious about new tools in software engineering and bring what you learn back to the team.
Requirements
- B.S. in Computer Science, Mathematics, or a related quantitative field (0-2 years of experience).
Qualifications
- Solid programming fundamentals in Python; familiarity with SQL.
- Experience using coding agents or other AI-assisted development tools.
- Strong problem-solving skills and a willingness to learn software engineering practices (testing, version control, deployment) on the job.
- Exposure to ML concepts (coursework or personal projects) is a plus.
Skills
- Python programming
- SQL
- Coding agents / AI-assisted development tools
Benefits
- Generous paid time off
- Competitive medical, dental & vision coverage
- 401K with company match for US
- Company-paid life insurance
- Company-paid short-term and long-term disability
- Access to mental health and wellness resources
- Company-paid volunteer time to do good in your community
- Legal coverage and other supplemental options
- A value-based culture where growth opportunities are endless
headings as required. No duplicate job title. No Pay/Schedule sections. We used
for prose paragraphs. We used
- for lists. We preserved all substantive information: company overview, job description, responsibilities bullets, requirements bullet (education), qualifications bullets (skills, experience, problem solving, ML exposure), skills bullets (Python, SQL, coding agents), benefits bullets. Check if any detail missed: The responsibilities bullet "Build real applications from day one" is a short phrase; we kept it. The bullet "Design, build, and maintain applications and services used across Snap's lending business, working alongside senior engineers who'll help you grow." kept. "Write clean, tested, well-documented code, and learn what it takes to take a project from an idea to something running reliably in production." kept. "Help bring machine learning into production" kept. "Work with our ML and Analytics teams to build the services and infrastructure that their models run on." kept. "Learn how production ML systems are built and maintained at scale, without needing to build the models yourself." kept. "Learn to connect engineering to business impact" kept. "Sit in on conversations with stakeholders across Analytics and the business to understand what problems your work is solving." kept. "Practice explaining technical decisions clearly to non-technical teammates." kept. "Build with modern tools" kept. "Use coding agents and other AI-assisted development tools to move faster from idea to shipped code." kept. "Stay curious about new tools