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About This Role
The details
- Location: Sweet James Headquarters (Newport Beach, CA), on-site
- Team: Technology
- Reports to: Chief Technology Officer
- Compensation: $200,000 to $250,000 base salary, depending on experience
The company
Sweet James Accident Attorneys is a rapidly growing, AI-powered personal injury firm based in Orange County, CA. In less than a decade, we've built a nationally recognized brand and a proprietary AI and automation stack that make us one of the largest and most efficient providers of legal services in the country. Our growth runs on a culture of excellence, compassion, and results: Our team is committed to exceptional client experience and to supporting one another in a fast-paced, collaborative environment. We invest in our people, encourage professional growth, and provide meaningful opportunities to build rewarding careers while making a real impact in our clients' lives.
The role
Technology is how we practice law, not a department beside it. We have built a highly automated, AI-forward experience for every client-facing role, from intake to settlement, and it is one of the leading examples of applied AI in the legal field: Our case teams run at a fraction of the headcount our largest peers need for the same work. The systems behind that result are built in-house, and you will be one of the people building them.
Sweet James is seeking a Lead AI Engineer to own the direction, standards, and health of the AI platform and to lead the engineers who build it. This is a player-coach seat for a staff-level engineer ready to manage: You will set the architecture, turn the roadmap into a build plan the team can execute, and remain hands-on in the code.
You will report to the Chief Technology Officer and own how the platform is built and run. The distinction from our Senior AI Engineer seat is scope: Senior engineers own features end to end; you own the system they fit into and the people who build it.
Responsibilities
- Direction: Own the architecture of the AI platform (e.g., document intake and filing, fact extraction, case search, the internal assistant and its agents, voice agents) and review every major design before it is built. The roadmap is set by the Chief Technology Officer and product leadership; you decide how it gets built.
- Standards: Define how the team ships, including code review, testing, and the evaluation sets every AI feature must clear before release.
- Platform health: Own uptime, monitoring, incident response, and access controls across the platform, and ensure no system depends on one person.
- People and capacity: Allocate the team's capacity against the priorities the Chief Technology Officer sets, make the trade-offs when demand exceeds it, and coach, review, and hire the engineers who carry the work.
- Delivery: Ship production code yourself every week.
- Business partnership: Decide with attorneys, case managers, and operations leaders which work is worth automating next, and report results in hours returned, error rates, and adoption.
- Data governance: Set the rules for least-privilege access, audit trails, and which client and medical data may reach which model.
Qualifications
Required:
- Experience: 8+ years building production software, with a track record as the technical lead on systems other engineers depended on.
- Leadership: Mentoring, design review, and ownership of a team's output; formal people management is welcome but not required.
- LLM craft: Systems shipped to real users spanning prompt and tool design, retrieval, structured extraction, evaluation, and cost and latency tuning.
- Stack: Strong Python and TypeScript/Node, production SQL in Postgres, Docker, containerized cloud services (Azure preferred), CI/CD, and monitoring.
- Operations: Ownership of uptime, including on-call, incident reviews, and rollbacks.
- Communication: Clear design docs, runbooks, and plain-language explanations for non-engineers.
Preferred:
- Models and tooling: Anthropic Claude and OpenAI APIs at production scale; agent frameworks or the Model Context Protocol; vector search (e.g., pgvector); self-hosted or open-weight models (e.g., vLLM, Baseten).
- Workflow automation: Platforms such as n8n.
- Salesforce: The data model and APIs; Litify a plus.
- Voice: Voice AI and telephony (e.g., Retell, Twilio, Zoom).
- Industry: Legal, insurance, or healthcare.
Pay: $200,000.00 - $250,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Work Location: In person
Salary Context
This $200K-$250K 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 Sweet James LLP, 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 ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
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.
Sweet James LLP AI Hiring
Sweet James LLP has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Newport Beach, CA, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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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