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About This Role
Who We Are:
Alpaca is a US\-headquartered self\-clearing broker\-dealer and brokerage infrastructure for stocks, ETFs, options, crypto, fixed income, 24/5 trading, and more. Our recent Series D funding round brought our total investment to over $320 million, fueling our ambitious vision.
Amongst our subsidiaries, Alpaca is a licensed financial services company, serving hundreds of financial institutions across 40 countries with our institutional\-grade APIs. This includes broker\-dealers, investment advisors, wealth managers, hedge funds, and crypto exchanges, totalling over 9 million brokerage accounts.
Our global team is a diverse group of experienced engineers, traders, and brokerage professionals who are working to achieve our mission of opening financial services to everyone on the planet. We're deeply committed to open\-source contributions and fostering a vibrant community, continuously enhancing our award\-winning, developer\-friendly API and the robust infrastructure behind it.
Alpaca is proudly backed by top\-tier global investors, including Portage Ventures, Spark Capital, Tribe Capital, Social Leverage, Horizons Ventures, Unbound, SBI Group, Derayah Financial, Elefund, and Y Combinator.
Our Team Members:
We're a dynamic team of 380\+ globally distributed members who thrive working from our favorite places around the world, with teammates spanning the USA, Canada, Japan, Hungary, Nigeria, Brazil, the UK, and beyond!
We're searching for passionate individuals eager to contribute to Alpaca's rapid growth. If you align with our core values—Stay Curious, Have Empathy, and Be Accountable—and are ready to make a significant impact, we encourage you to apply.
Your Role
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Alpaca is building an AI Enablement function to move the company from scattered AI experimentation to durable, company\-wide productivity gains. As Senior AI Platform Engineer, you will own the technical capability layer that makes that possible.
You will build and maintain the platform infrastructure, connectors, execution patterns, and self\-service tooling that lets engineering and business teams use AI safely at scale by turning one\-off setups and ad hoc tooling into reusable infrastructure, secure deployment standards, and productized onboarding.
This role requires hands\-on experience building with agentic AI systems. This is not classical ML pipelines, but the emerging stack of LLM\-powered agents, tool use, evaluation loops, and autonomous workflow execution. You should know this space well enough to make opinionated decisions about execution patterns, isolation boundaries, and what "safe and reliable" actually means for agents with real tool access.
This is a startup\-inside\-a\-scaleup role. You will need to move fast with limited resources while meeting the standards a regulated fintech environment requires. You will work closely with DevOps, Security, IT, and engineering teams across the company.
Things You Get To Do
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- Own the connector and service integration layer that powers AI workflows across the company.
- Design and ship execution environments for agents and higher\-autonomy AI workflows, including isolation boundaries and access controls.
- Build reusable platform services, golden paths, and self\-service templates that reduce setup friction for teams building on AI.
- Productize onboarding so it works reliably for both developers and non\-developers without depending on manual intervention or tribal knowledge.
- Define and enforce technical standards for agent execution, evaluation loops, and deployment.
- Partner with Security and IT to ship deployable patterns for higher\-risk AI capabilities.
- Own the AI governance layer: access controls, audit trails, approval criteria, and deployment boundaries for agentic workflows.
- Set the reliability, observability, and operational bar for AI\-specific infrastructure.
- Act as the technical escalation point when onboarding or platform issues block rollout.
- Reduce the company's dependence on individual heroics by turning exception handling into repeatable paths.
Who You Are (Must\-Haves)
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- 8\+ years in software, platform, infrastructure, or adjacent engineering roles.
- Hands\-on experience building agentic AI systems: LLM\-powered workflows, tool\-calling agents, evaluation loops, or autonomous execution — using frameworks like the Claude SDK, Google Agent Development Kit (ADK), LangGraph, or similar. Not classical ML or data pipelines.
- Direct experience with GCP. We run on Google Cloud and you should be comfortable there.
- Strong experience with APIs, auth, OAuth, secrets, CLI tooling, and deployment patterns.
- Cloud\-native systems experience with containers, orchestration (Kubernetes), and infrastructure\-as\-code.
- Experience implementing AI governance controls: access boundaries, audit logging, approval workflows, and safe deployment standards for higher\-autonomy systems.
- Comfortable operating in both fast\-moving, low\-process environments and more structured, compliance\-aware ones. You know when to move fast and when to slow down.
- Strong bias toward simplification, standardization, and operational reliability over clever one\-off solutions.
- Excellent communication skills with the ability to work across engineering, security, and non\-technical stakeholders.
Who You Might Be (Nice\-to\-Haves)
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- Shipped production agentic systems with real external tool access (filesystems, APIs, staging systems).
- Hands\-on experience with Google Agent Engine (Vertex AI Agent Builder) or equivalent managed agent execution platforms.
- Direct experience with AI\-native coding environments (eg. Cursor, Claude Code). You have strong opinions on how these tools fit into a real engineering workflow.
- Designed or operated agent sandboxing, isolation, or evaluation frameworks.
- Built self\-service developer platforms or golden paths used by multiple teams.
- Has startup experience and knows how to build durably with limited resources.
- Familiarity with fintech, regulated environments, or compliance\-aware deployment.
### How We Take Care of You:
- Competitive Salary \& Stock Options
- Health Benefits
- New Hire Home\-Office Setup: One\-time USD $500
- Monthly Stipend: USD $150 per month via a Brex Card
*Alpaca is proud to be an equal opportunity workplace dedicated to pursuing and hiring a diverse workforce.*
*Recruitment Privacy Policy*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AlpacaJapan株式会社, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AlpacaJapan株式会社 AI Hiring
AlpacaJapan株式会社 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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