Alt

• Own full-cycle recruiting across technical and non-technical roles — intake, sourcing, screening, interview coordination, offer, and close • Build creative sourcing strategies for niche, hard-to-find profiles: uncommon skill intersections, passive candidates, and talent pools others overlook • Deliver an exceptional candidate experience at every touchpoint — fast responses, honest communication, thoughtful rejections, and a process candidates rave about regardless of outcome • Run Greenhouse as a power user: pipeline hygiene, scorecards, structured interview plans, automations, reporting, and data the leadership team can act on • Lean into AI across the entire funnel — using Claude and other tools to build sourcing searches, personalize outreach at scale, synthesize interview feedback, and automate the repetitive parts of recruiting • Partner closely with hiring managers to sharpen role definitions, calibrate on profiles quickly, and keep searches moving with clear weekly updates • Use pipeline data to diagnose bottlenecks, improve pass-through rates, and continuously tighten time-to-fill • Help build Alt's employer brand and talent networks in the collectibles, fintech, and marketplace communities

• Optimize our pricing models to significantly reduce infrastructure costs while maintaining and improving their accuracy, especially for high-value assets. • Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates. • Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring. • Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements. • Design and execute experiments and backtesting to discover and validate new features that improve the models’ predictive power and coverage. • Own the models’ AWS infrastructure, writing code for our pricing APIs to ensure the models can serve at scale and with low latency.

• Own the activation funnel: Rebuild the submission funnel end to end, instrument every step, and run a real experimentation loop, including A/B testing and re-engagement for users who exit without submitting. • Scale acquisition channels: Figure out which channels actually acquire users on both sides (lifecycle, SEO, AEO, paid, mobile ads) and build the machinery to scale the ones that work. This includes agentic optimization: how Alt ranks and shows up in ChatGPT, Claude, Gemini, and other assistants. • Fix the data + attribution foundation: The unlock for everything else. Stand up clean attribution, consolidate our product analytics into a single source of truth, and modernize our messaging and deep-linking stack so we can finally tell what's moving the needle. • Build lifecycle infrastructure: Turn push, in-app, email, and SMS into an owned, instrumented system rather than per-campaign setups, including SMS opt-in compliance. • Balance the marketplace: Watch the balance across bidders, buyers, and sellers, and build growth into the weak side.

Role Description The Machine Learning Engineer will design, develop, deploy, and maintain advanced machine learning models and data analysis systems to support specialized domain modeling and proprietary feature engineering. The person in this role will: - Conduct applied research and experimentation to design, train, evaluate, and refine machine learning models, including performing feature engineering, selecting modeling techniques, validating model performance, and documenting analytical methods. - Develop, test, and deploy production machine learning systems by managing the complete MLOps lifecycle, including experiment tracking, model versioning, containerization, orchestration of automated workflows, and monitoring of model performance in production environments. - Maintain and optimize machine learning infrastructure to support training, inference, and data processing workflows, including configuring cloud compute environments, tuning distributed computation jobs, and improving system efficiency and scalability. - Collaborate with business stakeholders to translate analytical requirements into quantifiable modeling objectives, define evaluation criteria, validate assumptions with data, and communicate analytical findings and modeling results. - Design, prepare, and review technical documentation, including model design specifications, architecture diagrams, data-flow documentation, and systems integration requirements to support maintainability and long-term scalability. - Develop AI-driven automation solutions using Large Language Models to streamline internal workflows, design LLM-based agentic processes, validate automated outputs, and measure accuracy and efficiency improvements. - Design and develop internal software tools and backend services that support data quality, enable analytical workflows, expose model insights to internal teams, and integrate with organizational data systems and APIs. No travel is required. Fully remote position (100%) from anywhere in U.S. reporting to HQ in San Francisco, CA. Qualifications - Master’s degree (or foreign equivalent) in data science, economics, mathematics or computer science. - 1 year of experience in any occupations in which required experiences were acquired (may be pre-Master’s). Requirements - 1 year of experience designing, training, and evaluating machine learning models using Python-based data science libraries, including experience performing feature engineering and developing predictive and descriptive models using tools such as scikit-learn and pandas. - Experience using machine-learning lifecycle tools, including MLflow (or similar platforms) for experiment tracking, reproducible training workflows, and model versioning. - Experience using Docker for containerization to package machine learning pipelines and ensure reproducible deployment environments. - 1 year of experience orchestrating data processing and machine learning workflows, including scheduling, monitoring, and managing dependencies using Apache Airflow. - 1 year of experience using CI/CD tools to automate model training, testing, and deployment processes. - 1 year of experience configuring and optimizing cloud compute environments to support training, inference, and large-scale data processing tasks. - 1 year of experience developing AI-driven automation workflows using language models, including integrating language-based model components into analytical or operational processes. - Experience developing backend services and APIs using FastAPI, REST, or GraphQL to support data access, model serving, and analytical tooling. - 1 year of experience working with SQL databases, including PostgreSQL, and cloud data platforms (e.g., Snowflake), including writing analytical queries and implementing data-quality validation workflows. - Experience using distributed data-processing tools, including Spark, for large-scale data transformation and feature-engineering operations. Benefits - Salary offered: From $196,776 per year. - Job location: San Francisco, CA. To Apply Send resume to: Eryn@alt.xyz (write “Machine Learning Engineer” in subject line).

• Serve as the internal expert in card valuation and bringing nuanced understanding to pricing strategies for: basketball, baseball, football, pokemon. • Weigh in on detailed card questions, such as card values, nomenclature, history, and trends across many categories of cards including modern sports, vintage sports, and TCG. • Flexibility to work hours aligned with US time zones, demonstrating adaptability and commitment to global team collaboration • Meticulously enter and maintain card data in Alt's database, capturing intricate details, historical context, and market nuances • Work cross-functionally with Marketing, Customer Experience, and Operations teams to ensure data integrity and strategic alignment • Develop and refine pricing methodologies by combining market research, trend analysis, and deep category knowledge • Stay ahead of market trends by continuously monitoring sports card ecosystems, emerging collectibles, and transaction patterns • Innovate from within by leveling up on card strategies that help support Alt's vision.

• Automate the vault pipeline: Drastically reduce the time from item received to live on platform — building intake automation, scan orchestration, and quality-control tooling. • Expand beyond cards: Design and build ingestion, grading, and pricing workflows for non-card asset classes (watches, art, sealed wax) so Alt Values appear the moment items are vaulted. • Build Vault-as-a-Service: Architect the account infrastructure, billing, and APIs that let enterprise partners programmatically vault, ship, and manage assets at scale. • Ship the digitization stack: Optimize card imaging, lighting pipelines, and help turn static front/back photos into 360-degree views for digital repackers. • Own the full stack: Take features from technical design through polished UI — Vault touches real-world operations, and the product surfaces need to be as sharp as the backend. • Be a multiplier: Level up the growing Vault team through code reviews, technical documentation, and mentorship.

• Engage with collectors through chat, email, and phone support • Handle complex account questions and resolve issues with empathy and care • Document interactions and escalate issues as necessary for quick resolution • Collaborate with cross-functional teams to enhance service quality • Gather customer feedback to identify trends and suggest improvements

• Close business. Own the full sales cycle and bring high-end TCG buyers, sellers, and consignors onto Alt. • Hunt daily. We’ll start you with accounts, but your long-term success comes from sourcing new consignors and collectors across all major TCG formats—MTG, Pokemon, One Piece, Flesh & Blood, and beyond. • Build trust. In TCG, reputation is everything. You’ll become the go-to person that serious collectors—from kitchen table players to tournament grinders to whale investors—want to consign with, buy from, and sell to. • Shape product. You’ll spot gaps—whether it’s grading integrations, set-specific pricing tools, sealed product workflows, or buylist features—and bring those insights back to the team. • Run your process. Prospecting, qualification, relationship-building—it’s all muscle memory for you.

• Close business. Own the full sales cycle and bring sports card buyers and sellers onto Alt. • Hunt daily. We’ll start you with accounts, but your long-term success comes from sourcing new consignors and collectors across all sports card categories. • Build trust. In sports cards, reputation is everything. You’ll become the person serious collectors want to consign with, buy from, and sell to. • Shape product. You’ll spot gaps—whether it’s auction formats, pricing tools, or seller workflows—and bring those insights back to the team. • Run your process. Prospecting, qualification, relationship-building—it’s all muscle memory for you.

• Own the end-to-end seller journey from first submission to high-frequency, high-value seller • Identify and close drop-off points across onboarding, Snap to Sell, quote acceptance, vault intake, and resubmission • Build product-led experiences that activate dormant sellers and increase submission frequency among our dealer segment — without requiring manual outreach • Partner with Data and Lifecycle to deploy behavioral triggers and personalized experiences that drive submissions at scale • Drive search-to-bid conversion through better ranking, filtering, recommendations, and real-time Alt Value pricing context • Monitor and improve marketplace health — submission volume, listing rates, bid depth, and sell-through