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About This Role
Job Type: Full-Time Experience: 5–10 Years Seniority: Senior / Staff Level Location: New York City, NY or San Francisco, CA, USA Work Mode: On-site, 5 Days a Week Salary: $200,000–$300,000 USD per year + Competitive Equity Open Positions: 1–2 Visa Support: Eligible visa transfers considered Relocation: Available, including relocation assistance for eligible candidates moving to New York
Job Summary
We are seeking an exceptional Applied AI Engineer / Staff Agent Engineer to join our founding engineering team and build next-generation AI-powered systems for the industrial supply chain and staffing industry.
In this role, you will design, build, and scale AI agents that automate complex operational workflows, improve workforce matching, streamline candidate verification, and help businesses make better decisions using intelligent automation.
This is a high-ownership engineering opportunity for someone who has built and shipped AI agent products from scratch, enjoys solving complex technical challenges, and thrives in fast-growing startup environments.
You will contribute to both applied AI product development and foundational platform engineering, with the opportunity to influence architecture, technical strategy, and long-term product direction.
Key ResponsibilitiesApplied AI and Product Engineering
- Design and implement AI-powered agents to automate staffing and workforce management workflows.
- Build intelligent systems for worker sourcing, screening, verification, matching, and shift placement.
- Architect and develop scalable backend services, APIs, and distributed systems.
- Develop real-time matching and automated vetting pipelines using machine learning and AI agents.
- Own product features end-to-end, from problem discovery and technical design to deployment and optimization.
- Improve platform performance, reliability, scalability, and operational efficiency.
- Collaborate with product, engineering, and operations teams to deliver measurable business outcomes.
AI Agent Platform Engineering
- Architect and develop end-to-end AI agent platforms, including orchestration runtimes, tool integrations, and execution frameworks.
- Design model strategies, prompt systems, retrieval mechanisms, memory architectures, and agent evaluation frameworks.
- Build evaluation datasets, automated graders, regression suites, and experimentation tools to measure agent quality.
- Implement monitoring, observability, and reliability mechanisms for production AI systems.
- Integrate AI agents with internal and customer enterprise systems, including Warehouse Management Systems (WMS), Transportation Management Systems (TMS), and Enterprise Resource Planning (ERP) platforms.
- Establish best practices for AI-assisted software development and production deployment.
- Work directly with customers and operational teams to identify problems and convert insights into scalable product capabilities.
- Contribute to technical strategy, architectural decisions, and long-term platform development.
- Mentor engineers and support the growth of the engineering team.
Required Qualifications
- 5–10 years of professional software engineering experience, preferably in early-stage or high-growth startups.
- Proven experience building and launching 0-to-1 AI agent products in production environments.
- Demonstrated ownership of products or features from conception through deployment, with measurable business impact.
- Strong hands-on expertise in Python and/or TypeScript/Node.js.
- Experience designing distributed systems, backend services, APIs, and scalable data models.
- Strong proficiency with PostgreSQL and event-driven technologies such as Kafka, RabbitMQ, or similar messaging systems.
- Practical experience with AI agent orchestration, production deployment, evaluation, and observability.
- Strong understanding of the trade-offs between prompting, fine-tuning, retrieval-augmented generation (RAG), and conventional software logic.
- Experience implementing testing, monitoring, regression evaluation, and reliability practices for AI applications.
- Strong product mindset, problem-solving ability, and end-to-end technical ownership.
- Ability to operate effectively in a fast-paced, ambiguous startup environment.
- Bachelor's degree in Computer Science or a related technical field, preferably from a highly regarded university.
- Willingness to work on-site five days per week in New York City or San Francisco.
Preferred Qualifications
- Experience with AI-native marketplaces, staffing platforms, or multi-sided marketplace products.
- Background in matching algorithms, recommendation systems, workforce optimization, or credential verification.
- Experience building products for logistics, supply chain, manufacturing, healthcare, or other traditional industries.
- Familiarity with React, React Native, and modern full-stack development.
- Experience integrating AI agents with enterprise platforms and third-party APIs.
- Experience working directly with customers to understand operational challenges and translate them into technical solutions.
- Experience mentoring engineers and influencing technical architecture in a growing startup.
Ideal Candidate Profile
We are looking for a hands-on engineering leader who:
- Has a strong track record of building products from zero to one.
- Combines deep technical expertise with strong product judgment.
- Has shipped AI agent systems that operate reliably in production.
- Takes ownership of business outcomes rather than focusing solely on code delivery.
- Thrives in high-velocity environments and is comfortable making decisions with incomplete information.
- Demonstrates consistent career progression and meaningful contributions within previous teams.
- Enjoys collaborating across engineering, product, and operations.
Compensation and Benefits
- Annual base salary of $200,000–$300,000 USD.
- Competitive equity package.
- Relocation assistance available for eligible candidates moving to New York.
- Opportunity to build foundational AI technology with significant product and architectural ownership.
- Direct collaboration with senior leadership and cross-functional teams.
- Opportunity to influence the future of AI-powered staffing and industrial operations.
Work Location
New York City, NY or San Francisco, CA, USA
This is a full-time, in-person role requiring attendance at the office five days per week.
Visa Requirements
Eligible visa transfers are considered, including qualifying H-1B transfers and OPT candidates where applicable. New H-1B sponsorship through the lottery is not available. Candidates should confirm their current work authorization status during the initial screening.
Interview Process
The selection process may include:
- Recruiter screening.
- Hiring manager interview.
- Technical assessment and team interviews.
- Technical deep dives and cross-functional discussions.
- Final leadership interview.
How to Apply
If you are an experienced software engineer passionate about applied AI, autonomous agents, scalable systems, and building high-impact products, we encourage you to apply.
Please submit your updated resume highlighting your AI agent development experience, startup background, production deployments, technical ownership, and measurable product outcomes.
Join a high-growth engineering team building the next generation of AI-powered industrial automation.
Pay: $200,000.00 - $300,000.00 per year
Work Location: In person
Salary Context
This $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Svaraa Jewels, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Svaraa Jewels AI Hiring
Svaraa Jewels has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $300K - $300K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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