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About This Role
Insurance touches people during some of the most challenging moments in their lives. Hi Marley is on a mission to transform how the P\&C industry communicates, making those moments faster, easier, and more empathetic for carriers and the customers they serve. We build AI\-powered software that keeps everyone in the claims conversation informed and connected. If you believe insurance can combine operational excellence and automation with a human touch, we'd love to meet you.
As we continue to grow, we're looking for a Principal AI Platform Engineer to be a foundational hire on our new AI Operations team. This is the engineer who builds the *plumbing* of our internal agentic workforce: the AWS hosting layer, the Cognito\-backed identity model, the Accountability Registry that says who built what, the Agent Registry that governs who can act, and the Tool Catalog that lets our teams compose new capabilities safely.
You'll partner with the CTO and a UX\-focused builder to make Hi Marley an AI\-native company from the inside — and you'll do it on a strict AWS stack because our customers are insurance carriers who need real auditability, not a wrapper. If you love AWS Bedrock at the depth where Cognito, IAM, and CDK meet, want to define how an entire company governs agents, and have the communication chops to evangelize a platform internally, this is the role for you. *Teamwork and shared enthusiasm are a core part of our culture, which is why this role involves joining us in the Boston office for 2\-3 days each week.*
What You'll Do:
- Build the AI Operations platform on AWS: Own the hosting, deployment, and infra\-as\-code layer for every internal agent and tool we run. Bedrock for models, Cognito for auth, CDK or Terraform for everything else. No third\-party platforms — we are AWS\-only by design.
- Architect the registries and catalogs: Design and build the Accountability Registry (people, ownership, roles), the Agent Registry (identity, permissions, data scope, audit trail), and the Tool \& App Catalog (discover, register, approve). These are the spine of agentic governance at Hi Marley.
- Solve the identity\-inheritance problem: An agent acts with the inherited permissions of the human who invoked it. You'll design and ship the patterns — Cognito \+ IAM \+ scoped tokens — that make this real, deterministic, and auditable.
- Build the developer experience for internal agent builders: Templates, SDK helpers, deploy paths, observability, cost tracking. Make it so that every Hi Marley team can ship their own agent without reinventing security or hosting.
- Partner on security tooling with our Compliance \& IT lead: Implement the scans, audits, MCP governance hooks, and DLP signals that the AI Security Ops role will use. You build the rails; they build the gatekeeper.
- Evangelize the platform internally: Lunch\-and\-learns, internal docs, demo droplets, and "office hours" so other teams know what's available and how to use it. Communication and product instinct are as load\-bearing as the AWS depth.
- Use AI in your daily flow: Cursor, Claude Code, agentic coding tools — your default question is "could an agent do this?" before you write the code yourself.
What We're Looking For:
- A genuine curiosity about AI and emerging technologies, paired with the judgment to apply them thoughtfully and responsibly.
- Deep AWS expertise: You can architect a multi\-tenant platform on AWS without needing to outsource the hard parts. Bedrock, Cognito, IAM, EventBridge, Lambda, DynamoDB or Postgres, plus IaC (CDK or Terraform). Bonus: AgentCore, SageMaker, Workshop Studio.
- Identity \& authorization fluency: You've built systems where authorization is the architecture, not bolted on. OAuth 2\.1, scoped tokens, role inheritance, least\-privilege design — these are second\-nature to you.
- Strong product instincts, not just infra: You think about the developer experience of the people consuming your platform. You can design an SDK or a registry schema that other teams actually want to use.
- AI\-forward: You've shipped real LLM\-backed systems in production — not just prototypes — and have opinions about token cost, latency, evaluation, observability, and where determinism matters vs. where it doesn't.
- MCP / agentic systems literacy: Familiarity with Model Context Protocol or comparable agent\-tool patterns. You don't have to have shipped an MCP server, but you should be able to talk credibly about why MCP exists and what its security boundaries are.
- Excellent communicator: You can present platform work to engineers, executives, and customers, and write docs people actually read. This role only succeeds if the rest of the company knows what's available.
- Architectural integrity over framework hype: You'd rather build the right primitive on AWS than reach for a "magical" higher\-level abstraction. You can write Python, TypeScript, or Go fluently — the language matters less than the systems thinking.
Compensation, Benefits \& Perks:
At Hi Marley, we are committed to fair and transparent pay practices. The annual base salary for this role is expected to fall within the range of \[$152,000–$283,000], depending on experience, skills, qualifications, and location. Offers are determined based on these factors as well as internal peer equity. It's most common for new hires to start near the midpoint of the range, allowing room for growth as employees develop in their role.
In addition to base pay, we offer a comprehensive total rewards package that supports both your wellbeing and professional growth, including:
- Equity grants for all employees
- A 4% matching 401(k) program
- Medical, dental, vision, disability, and life insurance coverage for employees working 30\+ hours per week
- Monthly wellness stipend
- Paid parental leave
- A flexible vacation policy \- we all work hard and take time when we need it
Who We Are:
At Hi Marley, our culture is built on three core values that every employee embodies:
- Max Courage – We encourage our team, our customers, and their customers to dream big, try new ideas, and maximize impact by measuring risk.
- Be Humble – We lead with appreciation and promote a culture of humility, compassion, and openness to learn from anyone, anywhere.
- Ubuntu ("I am because we are") – We believe true success is much bigger than any single individual or company. By aligning our individual aims behind a shared purpose, we can achieve our fullest potential — together.
Life at Hi Marley:
Life at Hi Marley is shaped by collaboration, learning, and genuine connection. We're proud to foster an environment where people can do their best work, feel supported, and grow alongside a team that celebrates both individuality and shared purpose.
- A fun, lively startup culture that embraces creativity and innovation
- Core values\-based leadership that guides our decision\-making and daily interactions
- A culture of engagement, diversity, inclusion, and belonging — everyone's voice matters
- Flexible, hybrid work environment that values balance and trust
- Ample opportunities to learn, take on new challenges, and make an impact in a fast\-growing organization
- Meaningful work that directly supports our mission to help people and organizations communicate with empathy and clarity
*Hi Marley is proud to be an equal employment opportunity employer. We celebrate diversity and do not discriminate based on gender, sexual orientation, gender identity, religion, race, veteran status, disability status, or any other characteristic protected by applicable law. We are committed to building an inclusive work environment representing a variety of backgrounds, perspectives, and skills, where all employees are encouraged to be their authentic selves.*
*Hi Marley participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S. For more information, please review the documents under "E\-Verify Poster" here:* https://e\-verify.uscis.gov/web/OnlineResources.aspx
Salary Context
This $152K-$283K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Hi Marley, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($217K) sits 18% above the category median. Disclosed range: $152K to $283K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Hi Marley AI Hiring
Hi Marley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $283K - $283K.
Location Context
AI roles in Boston pay a median of $216,350 across 460 tracked positions. That's 8% above the national median.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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