Staff Software Engineer, Applied AI

Our Story

TopDog Law is a nationally scaling personal injury firm that owns the client experience end to end, from marketing and intake through litigation. We've grown 2-3x year over year for three years running, and we're now building the operations, systems, and teams to scale without sacrificing quality or culture. We're fully remote, and we value ownership, open communication, and speed. If you want to help build something exceptional, you'll feel at home here.

The Opportunity

We're looking for a Staff-level Software Engineer who is as comfortable designing production systems as they are applying AI to real business problems. This is a hybrid role, not a pure AI research or ML engineering position. You'll build the internal tools and platforms that power our operations teams, and be the technical expert on where AI creates real value and where it doesn't.

You'll set technical direction across multiple teams and systems, not just the ones you build yourself. When the business needs to explore an AI opportunity, weigh technology options, or understand where we stand on AI, you'll investigate, recommend, and deliver. You'll navigate ambiguity and make well-reasoned build-versus-buy decisions, then drive the recommended solution through implementation. You'll also think about the data each product generates, so what we build feeds our analytics and helps leadership decide where to invest.

You bring strong engineering judgment, a bias toward pragmatic solutions, and the ability to turn open-ended questions into decisions and working software. You'll be a trusted technical advisor to leadership, and your impact will be measured by what the whole team ships, not just what you build yourself.

What You Will Own

Architecture & Delivery

• Build and own. Architect and build internal tools and platforms for operations teams, owning them from design through deployment and ongoing operation, including reliability and incident follow-through.
• Set direction. Define service boundaries, integration patterns, and data flow across teams, and write the technical strategy and RFCs that make the direction clear.
• Simplify. Reduce operational toil by retiring or consolidating systems, managing tech debt and migrations, and weighing long-term maintenance cost in every build-versus-buy decision.
• Integrate and protect. Build reliable integrations with legacy and third-party SaaS systems, with security, access control, and audit trails designed in for sensitive data.

Applied AI

• Apply AI with judgment. Identify where AI belongs in a solution and where it doesn't, and design it as a well-engineered part of the system (retrieval, agents, LLM-powered features, workflow automation).
• Prove it, then ship it. Run time-boxed proofs of concept that answer "is this worth doing?" with evidence, then advance successful proofs of concept into production-ready solutions, with evaluation, observability, reliability, and security.
• Guide AI investment. Assess our AI maturity, define a practical roadmap, and track AI cost and ROI so investment decisions rest on data.

Data

• Build products that produce good data. Design data models and instrumentation so every product generates clean, usable data for analytics and strategic planning.

Technical Leadership

• Lead across competing priorities. Align teams with differing priorities on shared platforms, standards, and tradeoffs. Build consensus through influence and evidence, and drive decisions when consensus cannot be reached.
• Improve engineering quality and delivery practices. Write high-quality code and lead through code reviews, design reviews, and engineering standards, using AI coding tools (e.g., Claude Code, GitHub Copilot) to raise the whole team's delivery speed.
• Mentor and advise. Mentor engineers and serve as a trusted technical advisor to stakeholders and leadership, explaining tradeoffs clearly and driving decisions to closure.

What You Bring

Required:

• 10+ years of professional software engineering experience, with a track record of designing and shipping reliable, maintainable production systems.
• Demonstrated Staff or Principal-level scope: you have led architecture or a major technical initiative across multiple teams with competing priorities, and aligned them on shared platforms or standards.
• Hands-on experience building applications with LLMs and generative AI in production, not just experiments. Demonstrate experience operating AI systems at scale, addressing production challenges, and measuring outcomes.
• Working experience rolling out agentic systems into production, including evaluation, guardrails, and monitoring.
• Strong backend and systems design skills, including APIs, data modeling, cloud infrastructure, and integration with legacy and third-party SaaS systems.
• Reliability engineering experience, including SLOs, idempotency, retries, partial failure handling, and data consistency across integrated systems.
• Experience building systems that handle sensitive data, with sound security and governance practices such as access control, audit trails, and PII and privileged data handling.
• A track record of taking ownership of essential engineering work: tech debt strategy, migrations, and deprecation of systems that are no longer worth their cost.
• Experience designing data models and instrumentation that support analytics.
• Working knowledge of modern AI application patterns: RAG, embeddings, vector search, prompt, harness, and context engineering, tool use and agents, and evaluation methods.
• Ability to weigh build-versus-buy tradeoffs and balance technical options against cost, complexity, and business value, including choosing simpler solutions or stopping projects when the evidence doesn't support them.
• Strong communication skills, including explaining AI concepts and tradeoffs to non-technical audiences and driving decisions to closure.

Preferred (Nice to Have):

• Experience with vector databases and search technologies, whether managed services or extensions to existing databases.
• Experience with a modern data stack (e.g., BigQuery, Snowflake, dbt) and building the data pipelines that feed analytics.
• Experience building internal tools or platforms for operations teams.
• Familiarity with ML fundamentals, fine-tuning, or model evaluation frameworks.
• Experience with AI cost management, such as token usage, model selection, and caching.
• Background in a regulated or document-heavy industry such as legal or financial services.

Who Thrives Here (Core Values)

We don't hire on resumes alone. We hire for competence, character, and mindset.

• Embrace Change: Open to feedback, embracing change, and always asking "what's next?"
• Committed: You care deeply, have your teammates' backs, and show up to build something lasting.
• No-Ego Energy: Positive, professional, and solutions-oriented. Drama stays at the door.
• Ownership: You do what you say, follow through, and treat the business like it's yours.
• Fast & Hungry: You move with urgency, thrive under high expectations, and are motivated by growth and impact.

What We Offer

• Base Salary: $160,000 - $200,000, commensurate with experience
• Our job postings reflect the compensation range for the specific market and location of each role. Ranges vary by geography based on local market rates and cost of labor.
• Benefits include: Medical, dental, and vision insurance, 401(k) with company match, HSA, life insurance, disability coverage, paid time off, and parental leave.

Equal Opportunity Employer

We are an equal opportunity employer. Employment selection and related decisions are made without regard to age, race, color, national origin, religion, sex, disability, sexual orientation, gender identification, or being a qualified disabled veteran or qualified veteran of the Vietnam era or any other category protected by Federal or State law.

DISCLAIMER: This is a full-time employee position. It is not open to independent contractor or consulting arrangements. Compensation is determined by the location where the work is performed and is subject to applicable local law.

DISCLAIMER: Each TopDog Law affiliated entity may use may use artificial intelligence (AI) and automated decision-making tools to assist in screening and evaluating candidates for employment positions, including whether to advance a candidate in the application process. These tools assess job-related qualifications and characteristics-such as work experience, education, and skills relevant to the position as described in the job posting-based on information you provide in your application materials (e.g., resume, application responses, and other submitted materials, if any).

All adverse decisions affecting your candidacy are reviewed by a human member of our Talent Acquisition team before being finalized. AI does not make the final decision on your candidacy.

If you have questions about this notice, wish to request additional information about how AI was used in a decision affecting you, wish to request correction of inaccurate data, or need a reasonable accommodation that is required by law regarding TopDog Law's use of these tools, please contact: Talent Acquisition Department | [[email protected]