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Salary: USD 203,000 - 275,000 / annual
Altruist is transforming the wealth management industry with an AI platform for financial advisors. We're seeking an AI-native Staff Software Engineer to shape the technical direction of our data and AI platforms while remaining hands-on in building systems that help advisors deliver exceptional client service.
As a senior individual contributor, you will lead complex initiatives across teams, translating ambiguous business needs into practical technical plans, and building frameworks that other engineers can leverage. You'll own outcomes from design through production, balancing delivery speed with reliability, security, and cost.
Key responsibilities include:
- Setting technical direction for scalable data pipelines and data serving platforms, aligning architecture with business priorities
- Designing and building high-performance systems managing the full lifecycle of financial data (ingestion, transformation, serving, consumption)
- Creating shared frameworks and platforms that establish AI as a core company capability, including reliable data access for AI applications and agents
- Identifying opportunities to improve advisor experiences and internal workflows with AI, partnering with product and engineering teams
- Leading multi-team initiatives, clarifying technical tradeoffs and dependencies, and guiding delivery from design through rollout
- Establishing standards for data quality, observability, and recovery; partnering with Security on sensitive data protection
- Improving query performance, platform reliability, and infrastructure efficiency; resolving complex production issues
- Advancing effective AI use throughout the software development lifecycle while maintaining accountability for correctness and security
- Mentoring engineers, leading design reviews, and raising engineering standards through technical guidance and hands-on contributions
The company offers office spaces in Culver City, San Francisco, and Dallas, with a one-month work-from-anywhere policy. Benefits include premium healthcare, 401k with 4% match, 16 weeks paid parental leave, professional development budget, and financial guidance programs.
Requirements:
- 8+ years of software engineering experience with a track record of leading complex platform or distributed systems initiatives
- Strong proficiency in Python or Java; strong SQL skills; ability to design, build, debug, and operate production systems
- Deep experience with databases, data warehouses, ETL/ELT pipelines, and mature data processes at scale; understanding of data modeling, query optimization, and batch vs. real-time tradeoffs
- Experience with large-scale offline processing and batch pipelines using Spark or similar frameworks, including failure recovery and performance tuning
- Experience designing and operating cloud services, especially AWS, with working knowledge of Kubernetes, deployment practices, and observability
- Proficiency with AI-assisted development tools (Cursor, Claude Code) with sound judgment about limitations; ability to identify practical AI applications
- Experience setting architectural direction, writing technical proposals, building alignment across teams, and mentoring without formal authority
- Ability to turn ambiguous needs into clear priorities, make pragmatic build-versus-buy decisions, and balance immediate delivery with long-term platform needs
- Ownership mentality: responsibility for business outcomes, carrying changes through production, and supporting built systems
- Strong communication skills; ability to work effectively with technical and nontechnical partners
- Resilience and humility; ability to navigate uncertainty and adapt when evidence calls for it
- B.A. or B.S. in Computer Science or equivalent practical experience
Preferred experience:
- Snowflake, Airflow, or dbt in production environments
- Building and operating AI applications, agent frameworks, or shared services for governed data access
- Financial services data experience or environments where data accuracy, privacy, and reliability are essential
