Senior Back End Engineer, Hazel AI
AltruistSan Francisco, CAPosted 24 February 2026
Tech Stack
Job Description
About Altruist
Altruist is transforming the multi-trillion dollar wealth management industry by building an AI platform for wealth professionals. We partner with financial advisors nationwide, empowering them to grow, optimize time and resources, and deliver superior outcomes for their clients.
We're looking for exceptional talent to help us achieve our mission of making financial advice better, more affordable, and accessible to all. If you're passionate about challenging the status quo and want to do the most important work of your life, we'd love to meet you!
But first, our values
Kindness - Kindness doesn’t just equal niceness. We listen to understand. We embrace, and encourage healthy debate and diverse perspectives. We approach conflict openly, honestly, and respectfully.
Brilliance - Humility is the skill we’re most proud of and possessing a growth mindset is always top of mind. We take ownership in everything we touch; regularly using our unique superpowers to reach a common goal as a team. We succeed and fail as one.
Grit - When challenges arise, we stay laser focused on achieving our mission and finding a way forward, even when it’s hard. We are nimble and maintain a sense of urgency, swiftly adapting to change and overcoming obstacles.
The opportunity
We recently launched hazel.ai , an AI assistant that has ~10x’d users in one month and are excited to hire an AI engineer to build the AI engine at the heart of the product. This engine will:
connect to our users’ systems of record,
parse, extract and store structured and unstructured information and
deliver proactive insights that convert data into actionable outcomes (save time, taxes fees).
Your impact
Customer focused - Demonstrate an eagerness to
Deeply understand the industry, the opportunity and our users
Interact with them and learn about their needs and frustrations
Design and execution - Architect robust systems across data, model and product layers that bring our vision to life
Build scalable data pipelines to store, sync and extract large amounts of information from unstructured and structured data
Optimize our search and retrieval infrastructure to parse all relevant extracted information to produce grounded, accurate and low latency responses to user queries
Collaborate with product managers and subject matter experts to build steerable AI systems
Craft and maintain evaluation datasets, scoring modules and integrate them into our deployment pipeline to continuously monitor and improve the quality of our AI products
Relentless - Proactively identify, unblock and mitigate risks that can minimize our chance of timely success
Leadership - Act as a leader and steward of engineering best practices, mentoring other engineers towards maximizing impact
Curiosity - Stay ahead of the curve with latest developments in machine learning, AI and how it can be leveraged to build industry leading products
What you bring
Experience - 6+ years with some of them contributing towards building ML systems at scale, in a production setting. Have built and/or used
ranking or recommender systems,
generative AI products, or
vector databases, search indices, or other data stores for search and retrieval use cases
Education - Bachelors/Master's degree in Computer Science, Machine Learning, Data Science, or a related field
Foundations - A deep understanding of data structures, algorithms, and sound software engineering principles
Technical aptitude - You
have excellent problem solving skills,
can break down a complex problem into simpler constituent parts
know when to take a first principles approach and when not to reinvent the wheel
Intentional communication - intentional dialogue is a superpower. You listen as well as you share your perspective with others
Resilience - We’re inspired by your unwavering determination to achieve success, no matter the adversity you face along way
Bonus Points - Familiarity with statistical and mathema ... (truncated, view full listing at source)
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