[All remote jobs](https://jobicy.com/jobs.md)Open role[![Liftoff logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/c47aea9f-221.png)](https://jobicy.com/company/liftoff.md)Remote opportunity at[Liftoff](https://jobicy.com/company/liftoff.md) # Machine Learning Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/liftoff.md)Share21 Aug 2026Published30Listing views3Application actions20 Sep 2026Apply before Opportunity details ## About this role. AI SummaryLiftoff is hiring a Machine Learning Engineer for its Revenue Engine team to build statistical models and production systems for the mobile advertising marketplace. The role focuses on optimization, bidding innovations, margin allocation, advertiser budget retention, and experimentation in dynamic economic environments. Candidates need a PhD in a quantitative field, large-scale applied ML or economics experience, and strong production engineering skills. The position is remote-first within the United States, with California preferred and quarterly in-person gathering expectations. It is a senior, research-oriented engineering role requiring clear communication with both technical and non-technical partners. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis role requires doctoral-level quantitative expertise combined with the ability to productionize models for a complex, high-scale ad-tech marketplace. Success depends on independently designing experiments, reasoning about economic tradeoffs, and translating results into reliable systems. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$245,000US market range$215k–$275k0$303k AI insightThe disclosed annual base-salary offer ranges from $215,000 to $275,000 USD depending on approved-state location, with a midpoint of $245,000. The higher $235,000-$275,000 band applies to the SF Bay Area, Los Angeles/Orange County, NYC, and Seattle; the $215,000-$255,000 band applies to other approved locations. This range is competitive for a PhD-level machine learning engineer working on production optimization and marketplace economics in the U.S. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Statistical Modeling](https://jobicy.com/jobs?search_keywords=Statistical%20Modeling.md)[Econometrics](https://jobicy.com/jobs?search_keywords=Econometrics.md)[Experiment Design](https://jobicy.com/jobs?search_keywords=Experiment%20Design.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[Optimization](https://jobicy.com/jobs?search_keywords=Optimization.md)[Bidding Systems](https://jobicy.com/jobs?search_keywords=Bidding%20Systems.md)[Ad Tech](https://jobicy.com/jobs?search_keywords=Ad%20Tech.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Production ML Systems](https://jobicy.com/jobs?search_keywords=Production%20ML%20Systems.md) Sample interview questionsHow would you design an experiment to determine whether a new bidding strategy improves advertiser outcomes without harming platform margin?I would define primary metrics for advertiser performance and platform margin, establish guardrails for spend, retention, and delivery, and randomize eligible traffic at an appropriate unit such as campaign or auction cohort. I would run a power analysis, monitor treatment balance and interference, and evaluate both short-term lift and delayed effects. I would recommend rollout only if the strategy meets predefined performance and margin thresholds with statistically credible results. Describe how you would model price elasticity of demand in a mobile advertising marketplace. I would begin by defining the demand outcome and price signal, then account for confounders such as campaign objectives, seasonality, inventory quality, competition, and budget constraints. Depending on available variation, I would use controlled experiments or causal methods such as instrumental variables or panel models to estimate elasticities. I would validate the model across advertiser segments and use uncertainty estimates before incorporating it into optimization decisions. What practices would you use to turn a statistical model into a reliable production system? I would establish clear data contracts, offline evaluation criteria, reproducible training pipelines, and tests for features and model behavior. Before broad release, I would use shadow mode or a limited canary rollout, monitor prediction drift and business metrics, and define rollback conditions. I would also document assumptions, make decisions explainable, and maintain versioning for data, code, and models. How would you handle competing objectives between advertiser ROI, budget retention, and Liftoff's margin? I would frame the problem as constrained or multi-objective optimization, explicitly defining acceptable advertiser-performance guardrails and business constraints. I would evaluate tradeoffs by segment because a single global policy may not be optimal across campaigns or inventory. The resulting policy should be validated experimentally and regularly recalibrated as market conditions change. Explain a complex machine learning result to a non-technical GTM stakeholder. I would start with the business decision and outcome rather than the algorithm, for example: the new approach improved retained advertiser budget while maintaining target efficiency. I would use a simple visual or concrete comparison, describe key limitations and confidence, and state the recommended action. I would reserve technical details for follow-up while ensuring the stakeholder understands the expected impact and monitoring plan. Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand. Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence. About the Revenue Engine team The Revenue Engine team works to understand the fundamental economics of the mobile ad tech marketplace, including the elasticity of demand and the effects of competition. The team of machine learning engineers, software engineers, and data analysts develops theories, validates those theories with experiments and analyses, and uses the learnings to build production systems that improve outcomes for Liftoff and its advertisers. As a Machine Learning Engineer on the Revenue Engine team, you will: * Build statistical models and production systems to balance advertiser performance with business goals. * Tune optimization parameters, measure internal competition, and model dynamic environments. * Design and run experiments to validate theories underpinning the mobile ad tech economy. * Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations. * Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM). * Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences. * Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review. Requirements: * PhD in Computer Science, Machine Learning, Economics, or a related field. * Industry experience applying economics or machine learning to large scale problems. * Solid engineering and coding skills. * Excellent team communication and collaboration skills. * Experience with ad tech is a solid plus. Location: The preferred location for this role is within California. We are a remote-first company with US hubs in Redwood City, Los Angeles, and New York City. Travel Expectations: We offer several opportunities for in-person team gatherings, including but not limited to project meetings, regional meetups, and company-wide events. We expect our employees to attend these gatherings at least once per quarter. These gatherings provide essential opportunities for collaboration, communication, and team building. Compensation: Liftoff offers all employees a full compensation package that includes equity and health/vision/dental benefits associated with your country of residence. Base compensation will vary based on the candidate’s location and experience. The following are our base salary ranges for this role: * SF Bay Area, Los Angeles/Orange County, NYC, Seattle: $235,000 – $275,000 * All other cities and towns in our approved states: $215,000 – $255,000 #LI-EL1 #LI-REMOTE Liftoff offers a fast-paced, collaborative, and innovative work environment where employees are empowered to grow and make an impact. We’re shaping the future of the mobile app ecosystem—join us and help accelerate what’s next. Liftoff’s compensation strategy includes competitive salaries, equity, and benefits designed to support employee well-being and performance. We benchmark compensation based on role, level, and location to ensure fairness and market alignment. Benefits may include medical coverage, wellness stipends, and additional perks based on your country of residence. Liftoff is an equal opportunity employer. We are committed to creating an inclusive environment for all employees and applicants regardless of race, ethnicity, national origin, age, marital status, disability, sexual orientation, gender identity, religion, veteran status, or any other characteristic protected by applicable law. Agency and Third Party Recruiter Notice: Liftoff does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or Recruiting Team. All candidates must be submitted via our Applicant Tracking System by approved Liftoff vendors who have been expressly requested to make a submission by our Recruiting Team for a specific job opening. No placement fees will be paid to any firm unless such a request has been made by the Liftoff Recruiting Team and such a candidate was submitted to the Liftoff Recruiting Team via our Applicant Tracking System. 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