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market research & data analytics intern

software engineering intern (summer 2027) Jobs in San Francisco

ai ml intern

David Joseph & Company

<div>San Francisco, CA &middot; On-site &middot; Full-time 
Compensation: $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity<h3 class="jobSectionHeader">
About the Company</h3>
A fast-growing, venture-backed AI company building the training data and evaluation infrastructure that leading frontier AI labs use to improve their models &mdash; designing high-signal datasets and running rigorous evaluations that go beyond static benchmarks. One of the fastest-growing companies in its cohort.
Founded 2025 &middot; 11&ndash;50 people &middot; Industry: AI training data &amp; model evaluation infrastructure<h3 class="jobSectionHeader">
The Role</h3>
A founding-level role at the intersection of technical execution and strategic leadership. You'll own high-impact initiatives end-to-end &mdash; from requirements through delivery &mdash; managing teams of engineers, data scientists, and ML specialists while shipping production code yourself, and interfacing directly with research teams at leading AI labs.
What you'll be doing<ul><li>
Own end-to-end technical initiatives: scoping requirements, building solutions, managing experts through delivery, and shipping code yourself when it unblocks the team.</li><li>
Design and build technical infrastructure and tooling for dataset generation, quality control, and delivery pipelines across multiple concurrent lab contracts.</li><li>
Manage and mentor teams of software engineers, data scientists, and ML specialists; set technical direction and hold the bar for execution velocity.</li><li>
Interface directly with research teams at leading AI labs to translate their needs into technical specs and ensure delivery meets their standards.</li><li>
Debug production issues live under pressure; rapidly context-switch between hands-on engineering, team leadership, and strategic problem-solving.</li><li>
Support the founding team on the highest-impact work &mdash; internal tooling, major contracts, and hiring.</li></ul>
Tech stack: Python; data pipelines, APIs, ML/AI tooling<h3 class="jobSectionHeader">
Requirements</h3><ul><li>
CS degree, engineering experience, or equivalent demonstrated ability to ship technical projects</li><li>
Manage teams of technical experts across software engineering, data science, and machine learning</li><li>
Build technical infrastructure and tooling for dataset generation, quality control, and delivery pipelines</li><li>
Drive technical and operational initiatives end-to-end: scoping requirements, building solutions, and managing technical experts</li><li>
Interface directly with engineering and research teams at leading AI labs</li><li>
Support founding team on high-priority technical projects and operational initiatives</li><li>
Proficiency in Python and production-quality code</li><li>
Experience with data pipelines, APIs, or ML/AI tooling</li><li>
Strong leadership experience and communication skills</li><li>
Ability to context-switch rapidly between technical and strategic work</li><li>
Intense work ethic, agency, and drive</li><li>
Competitive nature and track record of exceptional achievement</li><li>
Undergraduate degree from the U.S., Canada, or the U.K.</li></ul><h3 class="jobSectionHeader">
Green Flags</h3><ul><li>
Background in software engineering, ML engineering, AI research, or computer science</li><li>
Experience at top tech company (FAANG, frontier AI lab) or quantitative trading firm</li><li>
Prior startup experience or founding experience</li><li>
Willingness to work long hours and weekends when projects demand it</li></ul><h3 class="jobSectionHeader">
Red Flags</h3><ul><li>
Worked cushy tech jobs (FAANG PM, etc.) and may not be used to intense work environments.</li><li>
Struggles with ambiguity or lacks examples of owning large, complex projects.</li><li>
Only interested in remote work &mdash; role is strictly in-person.</li><li>
Can't articulate why they want to pivot into AI ops or join a startup now.</li><li>
No experience managing teams or unclear on motivating people toward execution.</li></ul><h3 class="jobSectionHeader">
Why Join</h3><ul><li>
Direct access to the founding team on their highest-priority problems</li><li>
Exposure to leading frontier AI labs; work that powers major contracts</li><li>
Up to $300K total cash comp plus meaningful equity in a fast-scaling company</li></ul><h3 class="jobSectionHeader">
Details</h3><ul><li>
Location: San Francisco, CA</li><li>
Work policy: On-site &mdash; strictly in-person</li><li>
Compensation: $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity</li><li>
Visa sponsorship: None available</li><li>
Employment type: Full-time</li></ul></div>

Strategic Projects Lead - Coding

Smvsoft LLC

Founding Engineer - Applied AI San francisco, CA (Full time)- In-Office$180k-$230k per annumNO C2CAbout the RoleHiring a world-class Applied AI Engineer to build the core systems behind their automation engine for hardware engineering.This is a deeply technical, high-impact role. You will be among the first technical hires and will own mission-critical systems end-to-end. The problems are ambiguous, the constraints are real, and the expectations are high. If you move fast from idea to production, care deeply about customer experience, and want to build foundational AI infrastructure for the world's most impactful industries, this role is for you.Key Responsibilities<ul><li>Design and implement core workflows and infrastructure for hardware engineering agents, including intelligent context management, novel indexing approaches, and post-training</li><li>Build agents and ML models that can interpret, reason over, and manipulate complex engineering design data, integrating with enterprise-grade engineering tools and systems</li><li>Work closely with customers and the engineering team to deliver AI-powered workflows that are reliable, intuitive, and trusted in production environments</li><li>Prototype rapidly, validate ideas with real users, and ship frequently</li><li>Influence engineering culture, architecture decisions, and the future of the product</li></ul>RequirementsMust-Have<ul><li>Bachelor's or higher in Computer Science, Machine Learning, or equivalent experience</li><li>Strong familiarity with LLM architectures and agent research trends</li><li>Excellent programming skills with experience using ML libraries such as PyTorch, Hugging Face, and W&amp;B</li><li>Experience building production ML or AI systems</li><li>Solid practical experience with search and information retrieval</li><li>Moves fast from idea to production, with a genuine care for customer experience</li><li>Based in or willing to relocate to San Francisco, or an exceptional remote candidate</li></ul>Nice-to-Have<ul><li>2+ years in applied machine learning, backend engineering, or AI infrastructure</li><li>Experience working with machine learning over geometric data, including CAD</li><li>Track record of high-impact contributions or published papers at top-tier AI labs or teams</li></ul>Pay: $180,000.00 - $230,000.00 per yearWork Location: In person

Founding Engineer - Applied AI

<div>San Francisco, CA &middot; On-site &middot; Full-time 
Compensation: $120,000&ndash;$300,000 + Competitive Equity<h2 class="jobSectionHeader">
About the Company</h2>
An early-stage, venture-backed FinTech building AI compliance analysts that automate AML, KYC, KYB, and transaction monitoring end-to-end for banks and fintechs. The product operates inside browsers, APIs, and internal systems the way a human analyst would, and is already live with financial institutions across multiple regions.
Founded 2024 &middot; Seed stage &middot; Small team &middot; Industry: FinTech<h2 class="jobSectionHeader">
The Role</h2>
A backend-leaning Member of Technical Staff building the end-to-end systems that power production AI compliance workflows &mdash; high-performance infrastructure, reliability, and backend architecture supporting mission-critical automation.
What you'll be doing<ul><li>
Architect agent behaviors and build the backend systems behind production AI compliance workflows</li><li>
Build browser agents that navigate legacy systems, portals, and third-party platforms, interpreting unstructured data</li><li>
Process risk signals at the speed money moves &mdash; millions of transactions, sub-second decisions &mdash; via distributed inference, caching, queue orchestration, and self-healing data pipelines on AWS</li><li>
Contribute to an in-house deep-research pipeline that keeps LLMs from conflating similar entities</li><li>
Optimize ML pipelines and ship product features, owning problems vertically</li></ul>
Tech stack: Python, AWS; browser automation, computer vision, LLM systems.<h2 class="jobSectionHeader">
Requirements</h2><ul><li>
Experience with backend development and/or ML, especially in Python and AWS</li><li>
Hands-on experience deploying software to production environments</li><li>
Strong communication skills and willingness to interact with clients</li></ul><h2 class="jobSectionHeader">
Green Flags</h2><ul><li>
Experience with browser automation and computer vision</li><li>
Background with distributed inference and optimization on AWS</li><li>
Familiarity with LLM systems and deep research pipelines</li><li>
Experience building systems that process millions of transactions at scale</li><li>
Knowledge of compliance, AML, KYC, or financial crime domains</li><li>
Competitive coding background</li><li>
Contributions to open-source projects or personal projects on GitHub</li><li>
Prior experience as a founder or at an early-stage startup</li></ul><h2 class="jobSectionHeader">
Red Flags</h2><ul><li>
Candidates from a pure data science background who lack strong technical skills</li><li>
Lack of hands-on experience deploying software to production</li><li>
Primarily academic or research experience without practical engineering work</li></ul><h2 class="jobSectionHeader">
Why Join</h2><ul><li>
Build frontier AI agents in production, solving hard problems with no off-the-shelf answers</li><li>
Large, underserved market with real customer traction and measurable impact</li><li>
Small team with wide surface area &mdash; own features end-to-end across backend, ML, and product</li></ul><h2 class="jobSectionHeader">
Details</h2><ul><li>
Location: San Francisco, CA</li><li>
Work policy: On-site</li><li>
Compensation: $120,000&ndash;$300,000 + equity</li><li>
Visa sponsorship: H-1B, O-1, OPT</li><li>
Experience: 1+ years</li><li>
Employment type: Full-time</li></ul></div>

Member of Technical Staff

Decagon

<div>About Decagon
Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
Our technology enables industry-defining enterprises like Avis Budget Group, Block&rsquo;s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
We&rsquo;re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We&rsquo;re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
We&rsquo;re an in-office company, driven by a shared commitment to excellence and velocity. Our values &mdash; Just Get It Done, Invent What Customers Want, Winner&rsquo;s Mindset, and The Polymath Principle &mdash; shape how we work and grow as a team.
About the Team
The Security Engineering team at Decagon protects the platform that powers the most advanced conversational AI agents for enterprise customers across voice, chat, email and SMS. We build the security foundations that enable Decagon's AI agents to handle sensitive customer data with trust while defending against sophisticated, AI-enabled threats at massive scale.
Our mission is to provide magical support experiences &mdash; ensuring that AI agents and human agents can collaborate safely to help users resolve their issues while maintaining the highest standards of security and privacy.
About the Role
Lead the application security strategy and implementation for Decagon AI's conversational platform that serves enterprise customers at scale. You'll partner with engineering teams to build security directly into our AI-powered applications, ensuring protection against application-layer threats while maintaining the performance and reliability our customers expect. This role offers the opportunity to apply deep application security expertise to AI systems and shape security practices across our rapidly growing engineering organization.
In this role, you will<ul><li>
Design and implement application security controls across our AI agent platform, including secure coding practices, threat modeling, and vulnerability management.</li><li>
Collaborate closely with product engineering teams to integrate security throughout the software development lifecycle, from design, coding, PR, and deployment</li><li>
Establish application security testing programs including static analysis (SAST), dynamic analysis (DAST), and interactive testing (IAST) tailored for AI applications</li><li>
Lead security code reviews and architecture assessments for new features, with special focus on AI model integration points and customer data handling</li><li>
Build security tooling and automation to enable developers to identify and remediate vulnerabilities quickly while maintaining development velocity</li><li>
Respond to security incidents involving application vulnerabilities, coordinating remediation efforts and post-incident improvements</li></ul>
Your background looks something like this<ul><li>
Have 5+ years of hands-on application security engineering experience</li><li>
Expertise in secure software development practices, including threat modeling, secure code review, and vulnerability assessment</li><li>
Strong software engineering background with ability to review code across multiple languages and frameworks commonly used in AI/ML applications</li><li>
Experience implementing application security testing tools and integrating security into CI/CD pipelines</li><li>
Knowledge of OWASP Top 10, common application vulnerabilities, and modern application security frameworks</li><li>
Proven track record working with engineering teams to remediate security findings while balancing security and business requirements</li></ul>
Even better<ul><li>
Experience securing AI/ML applications, including prompt injection, model extraction, and adversarial input protections</li><li>
Background with large-scale, multi-tenant SaaS applications handling sensitive customer data</li><li>
Familiarity with Google Cloud application security services and container security best practices</li><li>
Knowledge of enterprise compliance requirements (SOC 2, ISO 27001, GDPR) from an application security perspective</li><li>
Experience with modern security tools like Semgrep, CodeQL, Cursor Bug Bot, XBOW, or similar</li></ul>
Compensation
$200K &ndash; $330K + Offers Equity
Benefits
We proudly offer the following benefits for our full-time employees:<ul><li>
Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)</li><li>
Medical, Dental, and Vision benefits for you and your family</li><li>
Life Insurance and Disability Benefits</li><li>
Retirement Plan (e.g., 401K, pension)</li><li>
Parental Leave</li><li>
Fertility and family building benefits through Carrot</li><li>
Daily lunches and snacks in the office to keep you at your best</li></ul>
These benefits are described in more detail in Decagon&rsquo;s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
Compensation Range: $200K - $330K</div>

Senior Security Engineer

Greptile

<div>
 We want to build agents that autonomously validate code changes. Today that looks like AI that reviews pull requests in GitHub, catching bugs and enforcing standards. We&rsquo;re reviewing close to 1B lines of code a month now for over 3,000 companies.
 
 
 
 Problems We&rsquo;re Excited About
 
 
 
 <ul>
 <li>
 Coding standards can be idiosyncratic and are often poorly documented; can we build agents that learn them through osmosis like a new hire might?
 </li>
 <li>
 Can we identify for each customer what types of PR feedback they do and don&rsquo;t care about, perhaps using some sample efficient RL, in order to increase signal-to-noise ratio?
 </li>
 <li>
 Some bugs are best caught by running the code, potentially against discerning AI-generated E2E tests. Can we autonomously deploy feature branches and use agents to parallel try to break the application to detect bugs?
 
 
 </li>
 </ul>
 
 Trajectory
 
 
 
 <ul>
 <li>
 7,000+ customers
 </li>
 <li>
 Raised $30M from Benchmark, YC, Paul Graham, Initialized
 
 
 </li>
 </ul>
 
 Team
 
 
 
 <ul>
 <li>
 We have assembled a small, talent dense team who have scaled critical functions at companies like Stripe, Google, Figma, LinkedIn, etc.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 BS in Computer Science or equivalent
 </li>
 <li>
 Some research experience, ideally with ML/LMs/Agents
 </li>
 <li>
 Strong programming skills and good product intuition
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Experiment with and apply recent advancements in agents/LMs etc. to improve the performance and capabilities of our products
 </li>
 <li>
 Example: you might study multi-agent architectures, prototype and evaluate a multi-agent code review workflow, and then work with a team to integrate successful prototypes into production systems
 </li>
 <li>
 Stay current with the latest research in LLMs, information retrieval, and developer tooling
 
 
 </li>
 </ul>
 Compensation Range: $180K - $280K
 
 
</div>

Research Engineer

Miso Labs

<div>At Miso Labs, our goal is to pass the voice Turing test. In order to do so, we must move beyond the current STT-&gt;LLM-&gt;TTS paradigm, and train full-duplex speech-to-speech models. This requires exploring new architectural ideas, and scaling them rapidly.
We&rsquo;ve raised a large seed round from top investors and are hiring our founding research team. We&rsquo;re a small team, so you can expect to work on every part of the model training stack from small-scale architecture experiments, to large pre-training runs on up to 1000 H100s. We are an in-person culture in San Francisco, and you will be expected to relocate to the SF area (we will cover the moving costs).
It is important to us that team members get credit for their work. Outside of our core IP, researchers are encouraged to publish their work in conferences, and we are happy to sponsor travel to top conferences in AI/ML.
Preferred Qualifications:<ul><li>
Have a Bachelor&rsquo;s Degree in a technical field (Computer Science, Physics, Mathematics etc.) or equivalent experience</li><li>
Excited to work on all aspects of model training and development</li><li>
Are comfortable with both research and hands-on engineering</li><li>
Are capable of owning long-term research outcomes end-to-end</li><li>
Willing to learn technical skills quickly, in a self-directed manner</li><li>
Prior experience with both PyTorch and CUDA</li></ul>
If you&rsquo;re interested in this role, please apply even if you don&rsquo;t fit all the qualifications. We&rsquo;re more interested in evidence of exceptional ability than prior experience in deep learning/AI.
This role comes with full benefits including:<ul><li>
Healthcare, vision, and dental</li><li>
Stipend for gym</li><li>
Relocation bonus</li></ul>
Compensation Range: $400K - $500K</div>

Founding Researcher

Ontrac Solutions

<div><h1 class="jobSectionHeader">Overview</h1>
We are seeking a Principal GenAI Architect / Forward Deployed Principal Engineer to lead the design, delivery, and productionization of enterprise-grade Generative AI solutions within a highly regulated banking environment. This role will partner directly with business, product, engineering, architecture, risk, cyber, legal, compliance, and model governance teams to translate high-impact financial services use cases into scalable, secure, and compliant AI systems.
The ideal candidate has deep experience designing and delivering production-scale GenAI solutions across enterprise environments, with strong exposure to banking, financial services, risk management, customer operations, regulatory controls, and data protection requirements. This person will serve as a recognized GenAI expert across the organization, helping define reusable patterns, influence platform roadmaps, and raise the technical bar for responsible AI adoption across lines of business.
<h2 class="jobSectionHeader">Key Responsibilities</h2>
<h3 class="jobSectionHeader">GenAI Architecture &amp; Production Readiness</h3>
Own and define enterprise-grade GenAI architectures for banking and financial services use cases, including RAG pipelines, agentic workflows, prompt orchestration, and multi-model routing strategies.
Drive production readiness for GenAI solutions, ensuring scalability, resiliency, observability, latency optimization, security, and cost efficiency.
Lead complex architectural decisions across data ingestion, vector databases, model selection, guardrails, API scalability, performance optimization, and secure integration with banking systems.
Establish reference architectures, reusable patterns, and best practices to accelerate responsible GenAI adoption across lines of business.
Partner with central AI platform teams to align solution architecture with enterprise AI services, platform capabilities, banking technology standards, and long-term roadmap priorities.
<h3 class="jobSectionHeader">Banking Use-Case Delivery</h3>
Act as a Forward Deployed Principal Engineer, partnering directly with business, product, and engineering teams to deliver high-impact GenAI use cases from ideation through production.
Translate banking and financial services business problems into clear AI system designs, technical requirements, and non-functional requirements with measurable outcomes.
Support GenAI use cases across areas such as customer service, operations, knowledge management, risk, compliance, fraud, employee productivity, document intelligence, and internal workflow automation.
Troubleshoot and resolve complex issues across non-production and production environments.
Partner closely with application teams to ensure AI solutions integrate effectively into existing banking platforms, enterprise workflows, data environments, APIs, and control frameworks.
Influence platform roadmap by feeding real-world banking use-case requirements back into central AI services and enterprise AI platform teams.
<h3 class="jobSectionHeader">Governance, Risk &amp; Compliance</h3>
Ensure all AI solutions align with banking risk, cyber, model governance, data protection, privacy, and regulatory expectations.
Partner with Cyber, Model Risk Management, Legal, Compliance, Risk, and Data Governance teams to design compliant AI patterns without slowing delivery.
Embed security, privacy, responsible AI, ethical AI, explainability, human oversight, and auditability into solution design by default.
Help define practical implementation patterns that balance innovation, speed, governance, regulatory expectations, and enterprise control requirements.
Ensure GenAI solutions are designed with appropriate guardrails for sensitive financial data, customer information, personally identifiable information, and regulated business processes.
<h3 class="jobSectionHeader">Organizational Influence &amp; Mentorship</h3>
Serve as a recognized GenAI expert across the enterprise, regularly consulted by engineering, architecture, product, risk, compliance, and leadership teams.
Mentor senior and staff-level engineers and help raise the technical bar across banking technology teams.
Contribute to internal communities of practice, architecture reviews, executive-level technical discussions, AI governance forums, and knowledge-sharing sessions.
Represent enterprise GenAI capabilities in internal innovation forums, banking technology discussions, and responsible AI showcases.
<h2 class="jobSectionHeader">Required Qualifications</h2>
7+ years of software engineering experience, with significant depth in AI/ML, data-intensive systems, distributed systems, or enterprise-scale platforms.
2+ years of hands-on Python programming experience.
2+ years of experience designing and delivering production-scale Generative AI systems in an enterprise environment.
2+ years of experience with LLMs, prompt engineering, and RAG architecture.
2+ years of experience with vector databases, semantic search, and retrieval systems.
2+ years of experience with API-driven, cloud-native architectures.
2+ years of experience with distributed systems, performance optimization, scalability, and reliability engineering.
Experience working within banking, financial services, fintech, or another highly regulated enterprise environment.
<h2 class="jobSectionHeader">Preferred Qualifications</h2>
Strong understanding of banking technology environments and non-functional requirements, including security, scalability, resiliency, observability, latency, auditability, and cost management.
Demonstrated Principal-level impact, with influence across multiple teams, platforms, business units, or lines of business.
Experience operating in highly regulated environments, with financial services or banking experience strongly preferred.
Hands-on experience with agentic AI frameworks, AI workflow orchestration, and multi-model routing.
Prior experience in a Forward Deployed Engineer, embedded engineering, or business-facing technical delivery model.
Experience partnering with risk, cyber, legal, compliance, data governance, or model governance teams to deliver production AI solutions.
Familiarity with banking controls, customer data protection, model risk management, audit requirements, regulatory expectations, and responsible AI principles.
Ability to communicate complex technical concepts clearly to senior executives, technical stakeholders, risk partners, compliance teams, and non-technical business leaders.
<h2 class="jobSectionHeader">Ideal Candidate Profile</h2>
The ideal candidate is a hands-on Principal-level engineer and architect who can move seamlessly between strategy, architecture, and execution in a banking environment. They are comfortable working directly with business teams to understand real-world financial services problems, while also going deep with engineering teams on system design, performance, security, governance, and production readiness.
This person brings strong technical judgment, enterprise banking delivery experience, and the ability to build practical, compliant GenAI solutions that can scale responsibly across a large financial institution.</div>

Principal AI Engineer

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