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Posted 6 months ago
Requirements and responsibilities
The RoleWe’re looking for a Machine Learning Engineer to help build and scale the next generation of Cognitiv’s ML infrastructure. As we transition from a legacy platform to a more modern, automated, and highly scalable system, you’ll play a pivotal role in designing and implementing the tools, pipelines, and practices that power our deep learning and real-time advertising platform.You’ll own the end-to-end ML lifecycle — from ingesting client data to developing, deploying, and monitoring models in production — while working closely with senior engineers, data scientists, and product stakeholders. This is a great opportunity for an engineer who’s eager to strengthen their skills in ML systems, MLOps automation, and distributed data workflows, and grow into a critical contributor on the team.What You'll DoDesign, automate, and optimize ML workflows including data ingestion, model training, deployment, and performance monitoring.Build and maintain scalable, cloud-native pipelines that support large-scale experimentation and high-volume model training and scoring.Own core components of our MLOps stack and drive improvements around reliability, scalability, and ease of use.Partner with cross-functional teams (Product, Engineering, ML Research) to align automation efforts with business needs.Write production-grade Python code, participate in code reviews, and ensure high-quality engineering standards.Enhance our observability, logging, and alerting infrastructure to improve operational resilience and reduce time-to-detection.Propose and experiment with new tools or workflows to help modernize our ML lifecycle and platform delivery.Tech StackLanguages/Frameworks: Python, PyTorch, PyTorch LightningCloud/Infra: AWS, Docker, Apache AirflowData: ClickHouse, S3, Spark, distributed data systemsModels: Deep Learning, LLMs, Hugging Face ecosystemWho You Are:Strong coder: You write clean, maintainable, and scalable code in Python.Hands-on builder: You have experience with ML pipelines, MLOps tools, or automation frameworks and thrive on improving workflows.Deep learning practitioner: You’ve trained models with PyTorch (bonus if PyTorch Lightning) and are curious about deploying large language models (LLMs).Cloud-native thinker: You’re familiar with AWS services, containerization, and orchestration tools like Docker and Airflow.Collaborative engineer: You enjoy problem-solving with cross-functional partners and communicate clearly across teams.Growth-driven: You’re eager to take ownership, deepen your technical expertise, and deliver high-impact work.In-office teammate: You’re available to collaborate in-person MTW in Bellevue WA or San Mateo CA.Bonus Points If You HaveExperience in AdTech or real-time bidding systemsExposure to ClickHouse, PySpark, or distributed data processing systemsUnderstanding of low-latency model serving architecturesAdvanced degree in Computer Science, Engineering, or related fieldLocation & CompensationLocation: Bellevue (hybrid: 3 days in-office, 2 days remote)Salary: $160,000-$220,000 Base Salary + EquityCompensation is based on experience, skills, and other factors. Base salary is just one part of your total rewards at Cognitiv—you’ll also receive equity and a comprehensive benefits package.Highlights include:Medical, dental & vision coverage (some plans 100% employer-paid)12 weeks paid parental leaveUnlimited PTO + Work-From-Anywhere AugustCareer development with clear advancement pathsEquity for all employeesHybrid work model & daily team lunchHealth & wellness stipend + cell phone reimbursement401(k) with employer matchParking (CA & WA offices) & pre-tax commuter benefitsEmployee Assistance ProgramComprehensive onboarding (Cognitiv University)…and more!ClosingCognitiv is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive workplace for all.
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