Interested in this AI/ML Engineer role at EvenUp?
Apply Now →About This Role
EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.
We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at www.evenuplaw.com.
As a Senior Product Designer at EvenUp, you'll help shape the future of how law firms acquire and intake new clients. You'll take ownership of the design for emerging 0-to-1 products and experiences across our First Mile AI product, transforming complex workflows into intuitive, AI-powered experiences that help law firms connect with and qualify new plaintiffs.
You'll work closely with product managers, engineers, data scientists, and ML researchers to understand customer needs, explore new product concepts, and ship thoughtful, high-quality experiences with high velocity. This is an opportunity to have significant influence on a nascent product area and help establish the design patterns and experiences that will shape the future of EvenUp's leads and intake platform.
This is a hybrid role with the expectation of working at least 3 days a week from our San Francisco or Toronto offices.
What you'll do:
- Own the end-to-end design process for 0-to-1 experiences across our leads and intake product, from discovery and early concepts through launch and iteration.
- Develop a deep understanding of law firm workflows, identifying opportunities to reduce friction and create meaningful improvements for case managers, paralegals, and lawyers.
- Design intuitive, efficient user flows for complex workflows including lead qualification, intake, outbound marketing, AI-assisted lead engagement, and referral experiences.
- Design AI-powered experiences that make complex technology feel simple, useful, and trustworthy, with thoughtful consideration for human-in-the-loop workflows, transparency, and guardrails.
- Partner closely with product managers, engineers, data scientists, and ML researchers to translate ambiguous problems into scalable product experiences and bring them to life.
- Conduct qualitative research and usability testing to understand user needs, validate concepts, and continuously improve the product experience.
- Create polished, high-quality interfaces using our design system while identifying opportunities to evolve patterns for new AI-powered workflows.
- Communicate design decisions clearly, bringing stakeholders along from early exploration through final execution and connecting your work to broader product goals.
What we look for:
- 5+ years of experience in software product design, with a track record of designing and shipping B2B SaaS products.
- Experience taking complex products or workflows from concept through launch, particularly in ambiguous or 0-to-1 environments.
- Proven experience designing AI-powered products, with a strong understanding of how AI, LLMs, and agentic workflows can shape user experiences. This is a requirement, not a bonus.
- Strong product thinking and systems thinking, with the ability to understand complex workflows and turn them into simple, intuitive experiences.
- Strong qualitative research and usability testing skills, with the ability to uncover what users actually need and translate those insights into design decisions.
- High proficiency in Figma and a strong eye for interaction and visual design.
- An execution mindset and the ability to move quickly while maintaining a high bar for craft and quality.
- Strong communication and collaboration skills, with the ability to work effectively with product, engineering, data science, and ML teams.
- Bonus: Experience designing conversational interfaces, AI agents, or other human-in-the-loop AI experiences, or working on products with complex data and workflows.
Benefits & Perks:
As part of our total rewards package, we offer attractive benefits and perks to our employees, including:
- Choice of medical, dental, and vision insurance plans for you and your family.
- Additional insurance coverage options for life, accident, or critical illness.
- Flexible paid time off, sick leave, short-term and long-term disability.
- 10 US observed holidays, and Canadian statutory holidays by province.
- A home office stipend.
- 401(k) for US-based employees and RRSP for Canada-based employees.
- Paid parental leave.
- A local in-person meet-up program.
- Hubs in San Francisco and Toronto.
(Please note the above benefits & perks are for full-time employees)
Notice to Candidates:
To ensure fairness and proper consideration, we do not accept resumes or expressions of interest via email or social media messages. If you’re interested in a role, please submit your application directly through our careers page*.*
Please note that EvenUp may use AI notetakers and other recording devices in the recruiting process. If you interview with us, with your consent, we may record your conversations and summarize them into notes for internal use. Recording is optional, and declining will not affect your candidacy.
EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Compensation Range: $117,901 - $160,000
Salary Context
This $117K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At EvenUp, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($138K) sits 34% below the category median. Disclosed range: $117K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
EvenUp AI Hiring
EvenUp has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span San Francisco, CA, US, San Francisco, CA, Toronto, Ontario, Canada. Compensation range: $160K - $160K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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