Job description

ABOUT ALLOCATE Allocate is transforming private market investing by enabling RIAs and family offices to seamlessly discover, model, and manage their private market exposure. Our platform combines curated fund and co-investment opportunities with institutional-grade infrastructure. Through a single, data-rich digital experience, clients access top-tier opportunities across venture capital, private equity, private credit, and other private asset classes—backed by powerful tracking, analytics, and administration tools. ABOUT THE ROLE Allocate is looking to add an Data Platform Engineer to the team! There's a lot for us to build, and we need a strong engineer with a broad skillset who can jump right in to help us lay the technical foundation for the company's future. ESSENTIAL RESPONSIBILITIES AND DUTIES - Build, train, and improve machine-learning models for production use. - Develop evaluation and feedback loops that measurably improve model performance over time. - Stand up the tooling to measure, monitor, and track model quality. - Evaluate new models and techniques, and bring the best into production. - Collaborate cross-functionally to ship ML-powered capabilities. MUST HAVE EXPERIENCE - 4+ years of applied ML / LLM engineering in production, with strong Python. - Experience taking models from prototype to reliable production systems. - Hands-on model fine-tuning and rigorous evaluation discipline. - Proficiency with Git and version control systems. - Hands-on experience with AI-assisted development tools such as Claude Code, OpenAI's Codex, or Cursor, and a demonstrated ability to incorporate new tools as they emerge. NICE TO HAVES - Applied NLP, computer vision, or multimodal models. - MLOps and model-monitoring tooling. - Background in fintech or financial data. EDUCATION - Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience. ESSENTIAL VALUES & CULTURE - Providing our clients with a world-class experience is our number one priority. We obsessively search for ways to improve the experience for our clients and partners—extraordinary response times, proactivity, and a top-tier experience in everything from product strategy to offline communications. - Challenge convention: Instead of detailing all the reasons an idea may not work, we question things to determine how a viable idea may be put into motion. - Commitment to continuous improvement: We find ways to personally scale each day by pushing ourselves up the learning curve. - Meritocracy, not politics: We place the utmost value on results and reward through merit, not political agendas. - Civil Discourse is embraced: Open, intellectually curious conversations are required to consistently arrive at the best decisions. Respect is paramount, but the mission is to get the right answer collectively, not to be right. - Embrace technological change: We adopt tools and techniques that make us faster, smarter, and better—especially around AI and automation—and drop outdated methods without hesitation. ADDITIONAL DETAILS - Location: Fully Remote (all I-9 eligible candidates will be considered). - Employment: Full-time. Seniority: Senior professional. - Salary: The expected base salary range for this role is $165,000 to $185,000. Actual compensation will be determined based on the candidate's primary work location and other job-related factors including skills, experience, qualifications, interview performance, internal equity, and market data. Candidates located in higher cost-of-living markets, including the San Francisco Bay Area, may be considered within the higher end of the range. This range reflects base salary only and does not include bonus, equity, or benefits; total compensation may also include a discretionary performance-based bonus. - Benefits: Medical, dental, and vision; 401(k); and responsible vacation time (PTO). - Travel required for team and department offsites. An in-person interview may be required during the process. A broadband internet connection is required. Compliance with Allocate's Code of Ethics is a given for this role.

Requirements

Must-haves

Build, train, and improve machine-learning models for production use.
Develop evaluation and feedback loops that measurably improve model performance over time.
Stand up the tooling to measure, monitor, and track model quality.
Evaluate new models and techniques, and bring the best into production.
Collaborate cross-functionally to ship ML-powered capabilities.
4+ years of applied ML / LLM engineering in production, with strong Python.
Experience taking models from prototype to reliable production systems.
Hands-on model fine-tuning and rigorous evaluation discipline.
Proficiency with Git and version control systems.
Hands-on experience with AI-assisted development tools such as Claude Code, OpenAI's Codex, or Cursor, and a demonstrated ability to incorporate new tools as they emerge.
Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience.
Challenge convention: Instead of detailing all the reasons an idea may not work, we question things to determine how a viable idea may be put into motion.
Commitment to continuous improvement: We find ways to personally scale each day by pushing ourselves up the learning curve.
Meritocracy, not politics: We place the utmost value on results and reward through merit, not political agendas.
Embrace technological change: We adopt tools and techniques that make us faster, smarter, and better—especially around AI and automation—and drop outdated methods without hesitation.
Location: Fully Remote (all I-9 eligible candidates will be considered).
Employment: Full-time. Seniority: Senior professional.

Nice-to-haves

Applied NLP, computer vision, or multimodal models.
MLOps and model-monitoring tooling.
Background in fintech or financial data.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlllmpythonai

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto