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About This Role
### About the Role
This is a founding\-level, senior individual contributor role at a Series A AI compliance SaaS company building the system of record for enterprise marketing and packaging review. The platform automates high\-stakes compliance workflows — turning manual, multi\-week approval cycles into hours — for large regulated\-industry brands.
As a Senior Applied AI Engineer focused on Agentic Systems, you will own the core technical moat: the deterministic orchestration layer and safety infrastructure that makes enterprise\-grade AI compliance trustworthy at scale. You are not building demos — you are building auditable, production\-grade systems where correctness is non\-negotiable.
This is a hybrid, in\-office role based in San Francisco, CA (3 days/week in\-office). You will work directly alongside a small, senior engineering team at a critical architectural inflection point.
### What You'll Do
- Agentic Reasoning \& Orchestration: Design and evolve multi\-agent LLM systems that decompose complex review tasks into reliable, auditable steps. Define agent responsibilities, hand\-offs, and termination conditions to minimize reasoning drift and maximize consistency.
- Context, Retrieval \& Memory Systems: Architect retrieval pipelines using RAG, structured memory, and graph\-based retrieval approaches to supply agents with the right context at the right time. Balance recall, precision, and latency across large knowledge bases (brand guidelines, regulations, historical decisions).
- Stateful, Asynchronous Workflows: Own long\-running, fault\-tolerant workflows using Temporal (or similar), ensuring retries, versioning, and determinism across non\-deterministic model calls. Treat agent orchestration as a distributed systems problem — managing state, failures, and observability.
- Evaluation, Safety \& Reliability: Build evaluation frameworks using statistical metrics, gold labels, and automated regression testing to prove system reliability. Prioritize correctness and trust, especially in high\-risk legal and compliance scenarios.
- Asset Understanding Pipeline: Collaborate on image and document preprocessing (OCR, layout analysis, Vision Language Models) to ensure downstream agents receive structured, machine\-readable context.
### What We're Looking For
Required
- 7\+ years of professional software/ML engineering experience
- Minimum 2\-year average tenure across roles, with a clear track record of career progression and promotions
- Demonstrated expertise architecting multi\-agent AI systems: agent role design, multi\-step reasoning flows, tool integration, memory/retrieval architectures, and deterministic/auditable workflows
- Hands\-on experience with LLM orchestration frameworks and stateful workflow engines (e.g., Temporal, Prefect, or equivalent)
- Strong background in retrieval\-augmented generation (RAG) and vector/graph\-based retrieval systems
- Experience building and owning production\-grade evaluation and safety frameworks for AI systems
- Startup experience *or* equivalent experience in CPG, regulated industries, or enterprise compliance domains
- Must be eligible to work in the United States without visa sponsorship
- Able and willing to work in\-office in San Francisco, CA at least 3 days per week
Nice to Have
- Experience with vision\-language models (VLMs), OCR pipelines, or document layout analysis
- Background in marketing technology, packaging compliance, or regulatory tech (regtech)
- Familiarity with enterprise compliance workflows or brand governance processes
- Prior experience as a founding or early\-stage engineer
### Compensation \& Benefits
- Base salary: $185,000 – $210,000 per year
- Equity participation (early\-stage, Series A)
- Hybrid work flexibility (3 days in\-office, remainder remote)
*Visa sponsorship is not available for this role. Candidates must be authorized to work in the United States.*
### Location
San Francisco, CA, United States — Hybrid (3 days/week in\-office required). Some remote flexibility on remaining days.
Salary Context
This $185K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CLERA, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $185K to $210K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CLERA AI Hiring
CLERA has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $170K - $250K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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