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Job Title: Machine Learning Engineer

Underdog.io

San Francisco · On-site Full-time Mid Level $175k – $288k/yr 1w ago

About the role

This company is the world’s first fitness organization, building the most advanced hardware, software, and AI technology to transform sleep into a personalized, data-driven recovery experience. Its products are trusted by high performers, professional athletes, and health-conscious consumers in more than 30 countries. Recognized multiple times as one of Fast Company’s Most Innovative Companies and twice named to TIME’s “Best Inventions of the Year,” the organization operates with a high-performance mindset—fast, focused, and driven by impact. Every role offers the chance to create cutting-edge technology alongside world-class talent, shaping a future where sleep becomes an active tool for living better. The team brings intensity to its work because the mission demands it. Team members embrace a mindset of relentless pursuit, pushing to be in the top 1% of their craft. This environment is not a 9‑to‑5; it is built for those who thrive under pressure, care deeply about the impact of their work, and want to do the most meaningful projects of their careers. The Role The organization is looking for a Machine Learning Engineer to build and ship consumer‑facing AI systems that power personalization, coaching, and next‑generation sleep intelligence. In this role, the individual will work across data, modeling, product, and engineering to translate research into reliable, measurable improvements for members. This position is ideal for someone who loves end‑to‑end ownership—from problem framing and prototyping through offline evaluation, online experimentation, production deployment, and iteration. How You’ll Contribute Build and deploy ML models that improve sleep experiences through personalization, prediction, and behavior understanding (e.g., readiness forecasting, event detection, individualized recommendations). Apply and adapt foundation‑model capabilities to real product workflows—including LLM tool use, retrieval‑augmented generation, multimodal modeling, and policy learning. Develop user behavior models that connect longitudinal signals (sleep, environment, routines) to actionable interventions, grounded in robust experimentation and measurement. Design evaluation strategies covering offline metrics, slice‑based analysis, calibration, reliability, and fairness; partner with product teams to run high‑quality online experiments. Productionize models by building scalable training and inference pipelines, and implement model monitoring, drift detection, alerting, and continuous improvement loops. Collaborate with cross‑functional partners across product, mobile, backend, and clinical domains to scope requirements and ship high‑impact features. What You’ll Need to Succeed Minimum Qualifications 2+ years of experience building ML systems in production, preferably for consumer‑facing products. Strong machine learning fundamentals across supervised learning, sequence and time‑series modeling, and modern deep learning. Hands‑on experience with large‑scale model training and evaluation using frameworks such as PyTorch, TensorFlow, or JAX, combined with strong Python engineering practices. Experience with personalization systems, including ranking, recommendations, segmentation, lifecycle modeling, propensity/behavior modeling, and causal or experiment‑aware thinking. Fluency with data tooling, including SQL, distributed compute (Spark, Ray), and cloud storage/compute environments. Strong product sense—the ability to translate ambiguous goals into measurable outcomes and iterate quickly with stakeholders. Bonus Points Experience applying LLMs or foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals). Experience with multimodal data (sensor signals combined with context) and/or health and biometrics data. Experience with privacy‑preserving approaches such as on‑device or federated learning, differential privacy, or data minimization. Experience designing experimentation frameworks or applying causal inference methods for personalization. Why Join Culture of Excellence Innovation is a standard, not an exception. The organization’s flagship product—a temperature‑regulated sleep system—is beloved by hundreds of thousands of customers worldwide. The team continuously pushes the boundaries of technology in sleep fitness, operating in a fast‑paced environment where excellence is expected and supported. Immediate Responsibility & Accelerated Growth From the first day, team members take on substantial responsibilities that directly influence core business and product success. As part of a small, empowered team, individuals own their projects and see the tangible impact of their efforts, creating a path that is both challenging and rewarding. Collaboration with Exceptional Talent The company brings together some of the brightest minds in the industry. Team members are experts in their fields and avid innovators who thrive in a dynamic, high‑performance environment. Equitable Compen...

Requirements

  • What You’ll Need to Succeed Minimum Qualifications 2+ years of experience building ML systems in production, preferably for consumer‑facing products
  • Strong machine learning fundamentals across supervised learning, sequence and time‑series modeling, and modern deep learning
  • Hands‑on experience with large‑scale model training and evaluation using frameworks such as PyTorch, TensorFlow, or JAX, combined with strong Python engineering practices
  • Experience with personalization systems, including ranking, recommendations, segmentation, lifecycle modeling, propensity/behavior modeling, and causal or experiment‑aware thinking
  • Fluency with data tooling, including SQL, distributed compute (Spark, Ray), and cloud storage/compute environments
  • Bonus Points Experience applying LLMs or foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals)
  • Experience with multimodal data (sensor signals combined with context) and/or health and biometrics data
  • Experience with privacy‑preserving approaches such as on‑device or federated learning, differential privacy, or data minimization
  • Experience designing experimentation frameworks or applying causal inference methods for personalization

Responsibilities

  • In this role, the individual will work across data, modeling, product, and engineering to translate research into reliable, measurable improvements for members
  • This position is ideal for someone who loves end‑to‑end ownership—from problem framing and prototyping through offline evaluation, online experimentation, production deployment, and iteration
  • How You’ll Contribute Build and deploy ML models that improve sleep experiences through personalization, prediction, and behavior understanding (e.g., readiness forecasting, event detection, individualized recommendations)
  • Apply and adapt foundation‑model capabilities to real product workflows—including LLM tool use, retrieval‑augmented generation, multimodal modeling, and policy learning
  • Develop user behavior models that connect longitudinal signals (sleep, environment, routines) to actionable interventions, grounded in robust experimentation and measurement
  • Design evaluation strategies covering offline metrics, slice‑based analysis, calibration, reliability, and fairness; partner with product teams to run high‑quality online experiments
  • Productionize models by building scalable training and inference pipelines, and implement model monitoring, drift detection, alerting, and continuous improvement loops
  • Collaborate with cross‑functional partners across product, mobile, backend, and clinical domains to scope requirements and ship high‑impact features
  • Strong product sense—the ability to translate ambiguous goals into measurable outcomes and iterate quickly with stakeholders

Benefits

Equitable Compen..

Skills

Machine LearningPythonPyTorchTensorFlowJAXSQLDistributed ComputeCloud Storage/Compute Environments

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