Description Sprout Social empowers businesses worldwide to harness the immense power and opportunity of social media in today’s digital-first world. Processing over one billion social messages daily, our platform serves up essential insights and actionable information to over 30,000 brands, informing strategic decisions that drive business growth and innovation, and fostering deeper, authentic connections to their end customers. Our full suite of social media management solutions includes comprehensive publishing and engagement functionality, customer care solutions, influencer marketing, connected workflows, and business intelligence. We're actively weaving AI throughout our products to drive our business’s growth trajectory. What you’ll do - Build and maintain infrastructure using AWS, Terraform, and Kubernetes to support AI/ML at scale, including Generative AI applications. - Manage the end-to-end lifecycle of machine learning models, ensuring observability and tooling support both scale and speed. - Execute at scale while staying nimble enough to keep up with new capabilities being offered by social network APIs. - Improve processes and champion ideas that matter while holding the team accountable to high code quality and engineering standards. - Support our AI/ML Scientists by developing tooling to streamline model development and deployment. What you’ll bring We’re looking for a creative, collaborative, pragmatic, highly motivated, and impact oriented technical leader to join our team in building great software. If you can solve hard problems, deliver quality server-side software, and confidently guide your peers to learn from and teach each other, we’d love to talk with you! The minimum qualifications for this role include: - 5+ years of experience developing and supporting AI/ML software in a production environment. - 5+ years of experience programming in object-oriented languages such as Java, Python, or C++. - Impact-oriented mindset with an interest in stability at scale and a willingness to engage in feature development. Preferred qualifications for this role include experience: - 3+ years of experience developing and supporting scalable, distributed backend services. - 3+ years of experience building and supporting GPU-heavy services. - 1+ years of experience with LLMs / Generative AI, including managing their unique costs, constraints, and observability challenges. - 1+ years of experience with Infrastructure-as-Code (Terraform) and container orchestration (Kubernetes) within AWS environments. How you’ll grow Within 1 month, you’ll plant your roots, including: - Complete Sprout’s New Hire training program alongside other new Sprout team members. - Get acclimated to the team's current Mission, Goals, and Objectives along with future product roadmaps. - Become familiar with the team’s existing deployment patterns and the ML Ops tooling ecosystem. Within 3 months, you’ll start hitting your stride by: - Decomposing work into small, similarly sized units and working with your squad to prioritize quarterly team goals. - Setting up initial software for model deployment and monitoring of ML models. Partnering with the Infrastructure team to deploy an existing ML model in Kubernetes. Acting as the domain owner for new projects and writing necessary design documents. Within 6 months, you’ll be making a clear impact through: - Rolling out monitoring and alerting tools to identify problems before they affect users. - Helping deploy new ML models processing hundreds of millions of messages a day. - Identifying technical debt and performance bottlenecks, and executing plans to improve the code. - Collaborating effectively across the organization to ensure big-picture alignment. Within 12 months, you’ll make this role your own by: - Becoming the go-to expert of ML Ops at Sprout. - Developing repeatable deployment patterns for data scientists to train, deploy, and evaluate batch, REST, and event-based ML services. - Owning cross-organizational projects and mentoring junior engineers to help them level up technically. - Surprising us! Use your unique ideas and abilities to change your team in beneficial ways. Our Benefits Program We’re proud to regularly be recognized for our team, product, and culture. We invest in our team with a comprehensive, competitive benefits program: - Comprehensive Health & Wellness: Premium BCBSIL medical, dental (high/low plans), and vision (Eyemed) insurance for you and your eligible dependents. - Premium Mental Health Support: Full, free access to Modern Health for you and your dependents, including coaching, therapy sessions, and digital wellness resources. - Retirement Savings: 401(k) plan with a 50% company match on your first 6% of contributions (a 3% total match). - Financial Security: 100% employer-paid Life and Disability insurance for your peace of mind. - Flexible Paid Time Off: A flexible PTO policy, supplemented with additional company-wide Rest & Recharge days throughout the year. - Paid Parental Leave: Up to 16 weeks of paid leave for new parents to support you in expanding your family. - Annual Lifestyle Stipend: A $1,000 USD annual Lifestyle Spending Account to spend on your physical, mental, and financial well-being. - Work From Home Support: A one-time $550 USD stipend to set up your home office, plus a monthly $50 USD stipend for internet. - Giving Back: 16 hours of paid volunteer time annually, plus a $100 annual match for your charitable donations. - Additional Financial Perks: Access to pre-tax commuter benefits, subsidized child/eldercare (Care.com), discounted pet insurance (Figo), and no-cost personalized financial wellness support through Your Money Line. *This list is for informational purposes only. Benefit offerings are discretionary and subject to change and do not constitute a contract or guarantee of benefits. Whenever possible, Sprout wants to provide our team with the flexibility to work in the location that makes the most sense for them. Sprout maintains a remote workforce in many places in the United States. However, we are not set up in all states, so please look at the drop-down box in our application to see whether your state is listed. Few roles require an office setting. If your position requires a physical presence in a Sprout office, it will be evident in the job listing and your offer letter. Individual base pay is based on various factors, including whether you’re located in Zone 1 or Zone 2, as well as relevant experience and skills. In the United States, we have two geographic pay zones. For this role, the expected base pay ranges for new hires are: - Zone 1 (New York, California, Washington): $149,300 - $205,260 USD annually - Zone 2 (All other US states): $135,700- $186,560 USD annually The listed ranges represent earning potential in this position. These ranges were determined by a market-based compensation approach; we used data from trusted third-party compensation sources to set equitable, consistent, and competitive ranges. We also evaluate compensation bi-annually, identify any changes in the market and make adjustments to our ranges and existing employee compensation as needed. Base pay is only one element of an employee's total compensation at Sprout. Every Sprout team member has an opportunity to receive restricted stock units (RSUs) under Sprout’s equity plan. Employees (and their dependents) are covered by medical, dental, vision, basic life, accidental death, and dismemberment insurance, and Modern Health (a wellness benefit). Employees are able to enroll in Sprout’s company’s 401k plan, in which Sprout will match 50% of your contributions up to 6% with a maximum contribution. Sprout offers “Flexible Paid Time Off” and ten paid holidays. We have outlined the various components to an employee’s full compensation package here to help you to understand our total rewards package. Sprout Social is proud to be an Equal Opportunity Employer. We do not discriminate based on identity- race, color, religion, national origin or ancestry, sex (including sexual identity), age, physical or mental disability, pregnancy, veteran or military status, unfavorable discharge from military service, genetic information, sexual orientation, marital status, order of protection status, citizenship status, arrest record or expunged or sealed convictions, or any other legally recognized protected basis under federal, state, or local law. Because Sprout Social is a federal contractor, we affirmatively recruit individuals with a disability and protected veterans. Learn more about our commitment to diversity, equity and inclusion in our latest DEI Report. If you require a reasonable accommodation for any part of the interview process or to submit your application, please email us at accommodations@sproutsocial.com. Include the nature of your request and your preferred contact information. We'll do everything we can to support your success during our recruitment process while upholding your privacy. Please note that only inquiries regarding accommodations will receive a response from this email address; other inquiries will not be addressed (e.g., you send your resume but are not requesting an accommodation). For more information about our commitment to equal employment opportunity, please click here (1) Equal Opportunity Employment Poster and (2) Sprout Social's Affirmative Action Statement. Additionally, Sprout Social participates in the E-Verify program in certain locations, as required by law. #LI-REMOTE Sprout Social Inc. and its subsidiaries process personal data submitted through your application to assess your qualifications for employment and to inform our hiring decision and, where applicable, for required governmental reporting. For more information, please review Sprout's Global Applicant Privacy Notice.

At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 450 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together? As a Staff Machine Learning Engineer, Personalization you will lead the technical charge to move us from static feeds to a "Cohort-First, Individual-Next" personalization strategy. Your work will directly impact Time-to-Bet and Deposit Velocity by ensuring no user has to scroll endlessly for relevant sports and markets based on their preferences. What you’ll do: - Architect the Hybrid Engine: Design and build the "Project Bridge" architecture, transitioning the platform from heuristic-based logic (Cohort/Geo-based) to fully real-time ML personalization (Vector Search/Neural Networks). - Real-Time Inference at Scale: Steer the design and deployment of low-latency services (Segment Service & User Profile Service) using Redis/DynamoDB to serve personalized board orderings, deposit defaults, and "For You" feeds in milliseconds. - Feature Engineering & Data Strategy: Partner with Data Science to build the logging pipelines that tag why a user saw an item (data labeling). You will create the feature store required to train future neural networks for individual-level personalization. - Solve the "Cold Start" Problem: Implement logic for dynamic league ordering and deposit smart-defaults based on geospatial data and initial user cohorts, ensuring immediate relevance for new users. What you have: - 7+ years of experience in Backend/ML Engineering with a specific focus on Recommendation Systems (RecSys) or Personalization engines in production. - 3+ years of technical leadership, acting as a lead and driving architecture decisions for high-traffic consumer applications. - Experience with Real-Time Data: Proficient in streaming architectures (Kafka/PubSub) and low-latency lookups (Redis, DynamoDB) to serve model inference in <200ms. - MLOps Experience: Experience with the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, or Databricks. - Strong Coding Skills: Expert in Python and SQL; proficiency in Go or Rust is a strong plus for high-performance inference layers. - Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE) or AWS equivalents. What makes you stand out: - Experience implementing "bandit" algorithms or reinforcement learning for content ranking. - Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading. - Experience building "Feature Stores" that bridge batch historical data with real-time event streams. Where you’ll live: - While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates. #LI-Remote Working at PrizePicks:The typical salary range for this position is $220,000 to $280,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process. This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting. Date Posted: 2/4/2026 1st Extension: 3/4/2026 Benefits you’ll receive:In addition to your great compensation package, full-time employees will be eligible for the following perks: - Company-subsidized medical, dental, & vision plans - 401(k) plan with company match - Annual bonus - Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!) - Generous paid leave programs, including 16-week paid parental leave and disability benefits - Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked - Company-wide in-person events and team outings - Lifestyle enhancement program - Company equipment provided (Windows & Mac options) - Annual performance reviews with opportunities for growth and career development You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

About Gusto At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff—like payroll, health insurance, 401(k)s, and HR—so owners can focus on their craft and customers. With teams in Denver, San Francisco, and New York, we’re proud to support more than 400,000 small businesses across the country, and we’re building a workplace that represents and celebrates the customers we serve. Learn more about our Total Rewards philosophy. About the Role: Gusto’s Data Science team leverages Gusto’s rich dataset to guide product direction and decision-making. We operate full-stack, conducting analyses, prototyping and deploying predictive models and statistical tools both for internal use and for our customers. For this role, we are looking for a technical leader (an individual contributor) to drive machine learning and AI in the payment and risk domains. You will build a model-driven risk platform to provide a trusted environment for Gusto Ecosystem. You’ll be working with an established team and seasoned payments and risk leaders in Engineering, Product, Design, Operation, Identity and Compliance. In this role, you’ll work cross functionally to build Platforms that span the entire breadth of the Payments and Risk Stacks, and use ML and AI to build a world- class, high secure platform that safeguards our users’ activities and money, and ensures unparalleled reliability. Here’s what you’ll do day-to-day: - Build and deploy machine learning models to identify, assess and mitigate risks - Responsible for driving research in the problem space, working with stakeholders to understand model requirements, developing the model from scratch, deploying the model alongside your engineering counterparts, and monitoring and maintaining the model’s performance over time - Partner with Engineering, Design, and Product counterparts in Payment and Risk to solve complex cross functional problems - Develop scalable frameworks and libraries that enhance and contribute to the team’s core analysis and modeling capabilities, including through the integration of LLMs to improve data processing, analysis, and insights. - Identify new opportunities to leverage data to improve Gusto’s products and help risk management team to understand business requirements and develop tailored solutions - Present and communicate results to stakeholders across the company Here’s what we're looking for: - 8+ years of experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning and artificial intelligence, including familiarity with Large Language Models (LLMs) and their applications. This could mean either a MS or PhD in a quantitative field with at least 5 years experience in a business environment, or BS or Data Science Bootcamp graduate with at least 8 years of experience working as a data scientist or a machine learning engineer in a business setting. - Proven experience in credit risk modeling or fraud risk modeling using logistic regression, random forest, Xgboost or neural networks, along with a strong understanding of AI-based approaches and the potential of LLMs to enhance traditional models. - Experience applying a variety of statistical and modeling techniques using Python, R or another statistical modeling language, as indicated by familiarity with many of the following techniques - predictive modeling, anomaly detection, ensemble methods, natural language processing (NLP, optional). Basic understanding of LLMs and their applications. - Strong programming skills - comfortable with all phases of the data science development process, from initial analysis and model development to deployment - Excellent communication skills - able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion - PhD or Masters plus equivalent experience in a quantitative field is a plus - Experience in the Fintech industry is a plus Our cash compensation amount for this role is targeted at $225,000 - $285,000 for San Francisco, New York, and Seattle, $205,000- $255,000 in Los Angeles, $187,000 - $235,000 in Denver, and $200,000 - $250,000 CAD for Toronto, Canada. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above. Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees who are based in those locations will be expected to work from the office on designated days approximately 2-3 days per week (or more depending on role). The same office expectations apply to all Symmetry roles, Gusto's subsidiary, whose physical office is in Scottsdale. Note: The San Francisco office expectations encompass both the San Francisco and San Jose metro areas. When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. This includes non-office days for hybrid employees. Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home at Gusto. Gusto is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Gusto considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Gusto is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. We want to see our candidates perform to the best of their ability. If you require a medical or religious accommodation at any time throughout your candidate journey, please fill out this form and a member of our team will get in touch with you. Gusto takes security and protection of your personal information very seriously. Please review our Fraudulent Activity Disclaimer. Personal information collected and processed as part of your Gusto application will be subject to Gusto's Applicant Privacy Notice.

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We’re looking for a Staff Machine Learning Engineer to join the Content Ranking team at Snap! What you’ll do: - Set and drive the technical direction for the Content Ranking team and advance the core ML capabilities of our personalized video recommendation systems - Design, build, and deploy machine learning solutions to support Snap's content recommendation systems, ensuring scalability, performance, and reliability - Own outcomes for large, cross-team ML initiatives, from problem definition through long-term iteration and impact - Establish technical vision and roadmaps, and resolve complex technical tradeoffs across teams - Influence and mentor engineers across org boundaries through technical leadership and collaboration Knowledge, Skills & Abilities: - Strong understanding of machine learning and deep learning approaches and algorithms, and their applications to content, recommendation, and/or search domain - Experience working for a team whose primary output is online ranking / recommendation models - Ability to design, train, and optimize advanced machine learning models - Ability to proactively learn new concepts and technology and apply them at work - Skilled at solving open ambiguous problems - Strong collaboration and mentorship skills Minimum Qualifications: - Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience - 8+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 7+ year of post-grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience - Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning Preferred Qualifications: - Experience in recommendation systems - Advanced degree in computer science or related field - Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks - Experience working with machine learning, ranking infrastructures, and system design - Experience working with LLM modeling or Generative Recommender systems - Experience working with modeling large-scale user interaction sequences​ If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information. "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable). Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! Compensation In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future. Zone A (CA, WA, NYC): The base salary range for this position is $229,000-$343,000 annually. Zone B: The base salary range for this position is $218,000-$326,000 annually. Zone C: The base salary range for this position is $195,000-$292,000 annually. This position is eligible for equity in the form of RSUs.