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About This Role
### About the Role
A well\-funded, early\-stage AI startup in the mechanical engineering software space is looking for a Staff Engineer — Agentic AI to own the core agent intelligence layer that turns engineers' intent into reliable, cost\-efficient multi\-step workflows across complex desktop engineering tools. This is a high\-impact, senior technical leadership role reporting directly to the CTO, sitting at the intersection of applied agentic AI, user research, and product delivery.
The company serves Fortune 100 hardware engineering customers and is backed by notable investors. You'll join a small, senior team and have a direct line to executive leadership. The role is on\-site in San Francisco, CA.
### What You'll Do
- Lead development of the core agent intelligence layer executing multi\-step workflows across complex desktop engineering software (CAD, CAE, PLM).
- Report to the CTO and serve as technical lead for a small team of AI engineers, a user researcher, and domain expert contractors.
- Own the full product loop: define agent capabilities from user stories, build implementations, and benchmark against real workflows.
- Drive agent task success rate — define the eval framework, establish baselines, and systematically improve completion metrics.
- Set and enforce per\-task token budgets; track cost per completed workflow to ensure commercial viability.
- Build rigorous, reproducible evaluation infrastructure grounded in validated user stories (SWE\-bench\-level rigor applied to engineering workflows).
- Lead user story mapping and validation through direct interviews and collaboration with domain experts.
- Translate validated user stories into testable evals, closing the loop between research and benchmarking.
- Own agent architecture decisions: tool\-calling strategies, state management, error recovery, model routing, and context management.
- Act as a player\-coach: write production code, review designs, unblock the team, and raise engineering standards.
- Collaborate cross\-functionally with integrations, product, and customers during POCs to align agent behavior with real\-world usage.
### What We're Looking For
Required (Dealbreakers):
- 7\+ years in software engineering, including at least 2 years building agentic LLM\-based agents that act in the real world (tool\-calling, multi\-step workflows, failure handling, cost constraints).
- Deep experience designing LLM application architectures: model selection, context/window management, retrieval strategies, tool\-calling frameworks, and orchestration patterns.
- Strong evaluation and benchmarking instincts for agentic systems — task completion, cost efficiency, failure mode analysis; familiarity with SWE\-bench, GAIA, or τ\-bench.
- Proven track record shipping AI systems with measurable outcomes (e.g., agent task success rate, cost efficiency) — not just demos.
- Strong Python skills and hands\-on experience with LLM tooling (function calling, tool use APIs, tracing/observability tools such as Logfire or LangSmith, evaluation frameworks).
- Experience leading a small technical team (3–6 engineers): setting direction, performing code reviews, driving architecture decisions.
Strongly Preferred:
- Experience with desktop automation, COM, or programmatic control of applications (beyond web APIs).
- Background in mechanical engineering, CAD/CAE, PLM, or adjacent industries.
- Familiarity with enterprise deployment constraints — agent behavior on locked\-down corporate workstations.
- Published work or open\-source contributions in agentic AI systems.
- Experience building or contributing to public benchmarks for AI agents.
### Compensation \& Benefits
- Salary: $160,000 – $250,000 USD annually, depending on experience.
- Equity participation in an early\-stage, Series A company.
- Note: Visa sponsorship is not available for this role.
### Location
- On\-site in San Francisco, CA, United States.
- This is not a remote role.
Salary Context
This $160K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($205K) sits 15% above the category median. Disclosed range: $160K to $250K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
CLERA AI Hiring
CLERA has 6 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $140K - $400K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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