Interested in this AI/ML Engineer role at Clear Street?
Apply Now →Skills & Technologies
About This Role
Clear Street is modernizing the brokerage ecosystem. Founded in 2018, Clear Street is a diversified financial services firm replacing the legacy infrastructure used across capital markets.
We started from scratch by building a completely cloud\-native clearing and custody system designed for today's complex, global market. Our platform is fully integrated with central clearing houses and exchanges to support billions in trading volume per day. We've agonized about our data model abstractions, created horizontal scalability, and crafted thoughtful APIs. All so we can provide a best\-in\-class experience for our clients.
By combining highly\-skilled product and engineering talent with seasoned finance professionals, we're building the essentials to compete in today's fast\-paced markets.
The Team:
The mission of the Clear Street Active team is to provide best execution for every asset class in every market. Active is currently building a new, state\-of\-the\-art, cloud\-based trading platform providing high\-performance traders access to liquidity venues across multiple asset classes, cutting\-edge charting capabilities, and sophisticated order handling with the flexibility to service both the active trader and institutional workflows.
The Role:
- You will build the core AI platform that powers a copilot inside a new, AI\-native trading experience
- You will develop a high\-performance backend in Rust that supports streaming responses, low\-latency tool execution, caching, and reliable orchestration of model \+ tools
- You will design robust APIs and runtime components for AI capabilities: authentication/authorization, rate limits, auditing, tracing, retries, and fallbacks
- You will build safe action infrastructure for trading workflows: deterministic order previews, confirmation flows, idempotent tool calls, and comprehensive audit logs
- You will partner with product and frontend to enable great AI\-native UX patterns (streaming, citations/grounding, "why" explanations, reversible actions)
- You will develop a deep understanding of the business domain and help translate it into secure, resilient platform primitives
Tech Stack: Rust (backend), Postgres, TypeScript, React / React Native, LLM APIs / model serving
Requirements:
- At least Eight (8\) years of experience and strong proficiency with any programming language
- Strong knowledge of systems programming fundamentals: concurrency, networking, performance profiling, reliability, and distributed systems patterns
- Experience designing and operating production APIs/services with strong observability, correctness guarantees, and security considerations
- Ability to work with stakeholders to define platform requirements, design the architecture, and deliver it end\-to\-end
- High degree of self\-motivation and willingness to jump into unfamiliar areas to solve problems
Bonus:
- Strong Rust experience in production (Tokio/async, service design, performance tuning)
- Experience building platforms for AI/ML inference, tool execution, streaming, or retrieval/grounding systems
- Experience in trading systems (order lifecycle, execution, risk checks, auditability) or other mission\-critical financial infrastructure
- Deep experience with trading across asset classes, margin types, etc.
- Experience with Postgres performance and data modeling in high\-throughput systems
We Offer:
The Base Salary Range for this role is $200,000 \- $350,000\. This range is representative of the starting base salaries for this role at Clear Street. Where a candidate falls in this range will be based on job related factors such as relevant experience, skills, and location. This range represents Base Salary only, which is just one element of Clear Street's total compensation. The range stated does not include other factors of total compensation such as bonuses or equity.
At Clear Street, we offer competitive compensation packages, company equity, 401k matching, gender neutral parental leave, and full medical, dental and vision insurance. In\-office benefits include lunch stipends, fully stocked kitchens, happy hours, a great location, and amazing views.
Our top priority is our people. We're continuously investing in a culture that promotes collaboration. We help each other through challenges and celebrate each other's successes. We believe that modern workplaces succeed by virtue of having high\-performance workforces that are diverse — in ideas, in cultures, and in experiences. We are proud to be an equal opportunity employer and put in the effort to make such a workplace a daily reality. \#LI\-Remote
Salary Context
This $200K-$350K range is above the 75th percentile 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 Clear Street, 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 ($275K) sits 26% above the category median. Disclosed range: $200K to $350K.
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.
Clear Street AI Hiring
Clear Street has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $350K - $350K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.