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About This Role
Member of Technical Staff — Context AI
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Location: San Francisco, CA (Onsite)
Compensation: $170,000 – $350,000 base \+ meaningful equity
Visa Sponsorship: H\-1B, O\-1, OPT supported
Experience Level: 1–15\+ years
Employment Type: Full\-Time
### About Context AI
Context is the AI platform redefining knowledge work. The team builds agents that continuously learn to capture companies' proprietary intelligence — procedures, data, and objectives — provides the work surface for those agents to perform complex, long\-horizon tasks alongside humans within a native office suite, and deploys them in secure environments for Fortune 500 companies.
The team includes engineers and researchers from Apple, Ramp, Stripe, Meta, BAIR, and SAIL — having built applications with 1M\+ users, launched campaigns reaching 180M\+ people, and forward\-deployed products within some of the world's largest teams. Backed by Lux Capital, Qualcomm Ventures, General Catalyst, and BoxGroup, with $8–10M in revenue across five Fortune 500 customers on 2–3 year average deals.
Features shipped this week land in Fortune 100 hands next week — every product bet gets immediate, high\-stakes signal from the most consequential corporate teams in the world.
### About the Role
As a Member of Technical Staff, you will think end to end about what it means to build the most potent enterprise AI agents and design the best interface for deploying them — with broad autonomy to work wherever you believe the highest leverage lies across the entire stack. You own entire features, your code ships to real customers fast, and your product and technical decisions shape the course of the company.
Two tracks are available:
- Forward\-Deployed (FDE) Track: Full\-stack TypeScript/React with direct Fortune 100 customer engagement
- Platform Track: Deep infrastructure ownership across AWS, Terraform, and Kubernetes at enterprise scale
### What You'll Own
- Ship features end to end across the full\-stack TypeScript/React application — from design through implementation to production
- Make high\-judgment calls about what to build next based on customer needs, technical debt, and product opportunity
- Work directly with Fortune 100 customers to understand their workflows and translate that into product (FDE track)
- Build the underlying infrastructure (AWS, Terraform, Kubernetes, security) that scales Context to enterprise deployment (platform track)
- Contribute across the stack wherever you see the most leverage
- Shape engineering culture and practices at an early\-stage company where decisions have outsized impact
### Requirements
- Has shipped LLM and agent systems past RAG — built real agentic workflows in production, not just RAG demos. Pure RAG\-only candidates do not clear.
- Strong full\-stack engineering with production TypeScript and React (FDE track) OR deep infrastructure background with AWS, Terraform, Kubernetes (platform track)
- Track record of shipping product — focus is on what has been built, not years of experience
- High agency — does not wait to be told what to work on
- Comfort with ambiguity and moving fast in a small team
### Nice to Have
- AI/ML systems, LLM integrations, or agent framework production work
- Enterprise customer experience or forward\-deployed engineering background
- Bachelor's from an Ivy\+, Berkeley, Stanford, Georgia Tech, or UT
- Last 2 YC batch founder background (FDE track)
### Interview Process
- Pending approval
- Intro call with Sasank (founder)
- Technical round
- 1\-day work trial
- Offer
### Logistics
- Role is fully onsite in San Francisco — please only apply if you can commit to this
- H\-1B, O\-1, and OPT visa sponsorship available
Shortlisted candidates will be contacted by David Joseph \& Co., the recruiting partner managing this search on behalf of Context AI.
Salary Context
This $170K-$350K range is above the 75th percentile 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 David Joseph & Company, 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 ($260K) sits 45% above the category median. Disclosed range: $170K to $350K.
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.
David Joseph & Company AI Hiring
David Joseph & Company has 18 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer, AI Agent Developer. Positions span Philadelphia, PA, US, San Mateo, CA, US, Austin, TX, US. Compensation range: $165K - $350K.
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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