Jobs in Cambridge

About Liquid AISpun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.The OpportunityThis is a rare chance to apply frontier sequential recommendation architectures to real enterprise problems at scale. You will own applied ML work end-to-end for recommendation system workloads, adapting Liquid Foundation Models for customers who need personalization and ranking capabilities that run efficiently under production constraints.Unlike most recommendation roles that are siloed into a single product surface, this role gives you full ownership over how large-scale recommendation models are adapted, evaluated, and deployed for enterprise customers. Between engagements, you will build reusable applied tooling and workflows that accelerate future delivery.If you care about data quality at scale, user behavior modeling, and making recommendation systems actually work in enterprise production environments, this is the role.What We’re Looking ForWe need someone who:Takes ownership: Owns customer recommendation system engagements end-to-end, from requirements through delivery and evaluation.Thinks at scale: Can reason about user interaction data, sequential modeling, feature engineering, and evaluation across large-scale production systems.Is pragmatic: Optimizes for measurable customer outcomes (engagement, conversion, revenue lift) over theoretical novelty.Communicates clearly: Can translate between customer business metrics and internal technical decisions, and push back when needed.The WorkAct as the technical owner for enterprise customer engagements involving recommendation and ranking workloadsTranslate customer requirements into concrete specifications for recommendation modelsDesign and execute data pipelines for user interaction data, feature engineering, and training data curation at scaleFine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use casesDesign task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret resultsBuild reusable applied tooling and workflows that accelerate future customer engagementsDesired ExperienceMust-have:Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering)Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systemsStrong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment)Experience with large-scale data pipelines for user interaction data and feature engineeringProficiency in Python and PyTorch with autonomous coding and debugging abilityNice-to-have:Experience with transformer-based recommendation architectures (HSTU, SASRec, BERT4Rec, or similar)Experience delivering recommendation systems to external customers with measurable business outcomesFamiliarity with serving recommendation models under latency and throughput constraintsWhat Success Looks Like (Year One)Independently owns and delivers enterprise recommendation system engagements with minimal oversightIs trusted by customers as the technical owner, demonstrating strong judgment on the tradeoffs between model quality, latency, and business impactHas built reusable applied workflows or tooling that accelerate future customer engagementsWhat We OfferReal ML work: You will build and adapt large-scale recommendation models for enterprise customers, working with frontier architectures like HSTU under real production constraints.Compensation: Competitive base salary with equity in a unicorn-stage companyHealth: We pay 100% of medical, dental, and vision premiums for employees and dependentsFinancial: 401(k) matching up to 4% of base payTime Off: Unlimited PTO plus company-wide Refill Days throughout the yearPlease mention the word LYRICAL and tag ROjox when applying to show you read the job post completely (#ROjox). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.

Join Cartwheel to help tackle the student mental health crisis. Cartwheel is an early-stage company building a new kind of mental health program for kids that puts schools at the center. We see our role as supporting school staff who see kids every single day. Instead of going around them, we collaborate with them. This means: Earlier intervention Higher student and family engagement in care Better coordination among the trusted adults in a student's life Kids shouldn't just aspire to get out of bed and drag themselves to class. They should be able to experience joy. They deserve to envision and build a life they're excited to live. If you join Cartwheel, you'll help make this vision a reality for millions of students across the country. We're backed by top investors including Menlo Ventures, Reach Capital, General Catalyst, BoxGroup, and Able Partners, and we're looking for mission-driven teammates to join our team. ABOUT THE ROLE We are seeking a highly experienced Certified Medical Coder with deep expertise in Medicaid and Managed Care Organizations (MCOs), advanced modifier application, and research of new/complex codes. This role will lead coding strategy and execution for expanding billable services, ensuring accuracy and compliance while enabling scalable automation. You will partner closely with the RCM Director to translate complex coding requirements into operational workflows and systems. Role type: 1099 Contractor, 40 hours a week (M-F, EST hours) Pay range: $30-40/hr Location: Remote WHAT YOU'LL DO Advanced Coding Accuracy & Optimization Perform complex coding for CPT, HCPCS, and ICD-10 with a focus on high-impact, payer-sensitive services Lead expansion and validation oPlease mention the word TERRIFICALLY and tag RODguMTk4Ljk5LjE0Mw== when applying to show you read the job post completely (#RODguMTk4Ljk5LjE0Mw==). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.