Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.
Machine Learning Engineer
We're looking for a talented and motivated Machine Learning Engineer to join our team and help develop cutting-edge AI solutions. In this role, you'll have the opportunity to shape and create our machine learning capabilities from the ground up. You'll be at the forefront of innovation, designing and implementing ML systems that drive our business forward.
Responsibilities:- Design, develop, and implement machine learning models and algorithms
- Analyze large datasets and extract meaningful insights
- Collaborate with cross-functional teams to integrate ML solutions into existing systems
- Optimize ML models for performance and scalability
- Stay current with the latest advancements in machine learning and AI
- Create and implement big data processing pipelines and architectures
- Design and build scalable data infrastructure to support ML applications
- US Citizen
- Bachelor's or Master's degree in Computer Science, Data Science, or related field
- 3+ years of experience in machine learning or AI development
- Strong proficiency in Python and its ML/data science libraries (e.g., TensorFlow, PyTorch, scikit-learn, pandas)
- Solid understanding of machine learning algorithms and statistical modeling
- Experience with big data technologies (e.g., Hadoop, Spark, Kafka)
- Proven ability to create and implement big data solutions from scratch
- Comfortable setting up and managing distributed computing environments
- Experience in designing and implementing data pipelines for large-scale data processing
- Proficient in working with various database systems, both SQL and NoSQL, depending on the use case
- Strong skills in data modeling and database design for machine learning applications
- Excellent problem-solving and analytical skills
- Strong communication skills and ability to work in a team environment
- Experience in creating and managing real-time data streaming architectures
- AWS Certified Machine Learning - Specialty or AWS Certified Big Data - Specialty
- Experience with AWS machine learning services
- Proficiency in using AWS big data services
- Knowledge of serverless architectures on AWS (e.g., Lambda)
- Experience in creating and managing real-time data streaming architectures on AWS (e.g., Kinesis)
- Understanding of AWS security best practices for machine learning and data processing workflows
- Be a member of a world-class team focused on inventing solutions that have the ability to impact the world
- Tackle a wide variety of technical problems throughout the stack and contribute daily to all parts of our product code base
- Help build a beautiful, intuitive product that revolutionizes the nonprofit industry
- Work closely with our customers, founders, team members and Board to understand customer pain points, develop solutions, and prototype, iterate, and deploy code on regular cycles
Similar Jobs
Cloud • Fintech • Food • Information Technology • Software • Hospitality
Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.
Top Skills:
GoJavaKotlinPythonScala
Artificial Intelligence • Machine Learning • Natural Language Processing • Software • Conversational AI
Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.
Top Skills:
Ci/CdCloudDistillationGpuPythonPyTorchQuantization
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.
Top Skills:
Api IntegrationsAutogenAWSAzureAzure Ai SearchChromaContainersCrewaiElasticsearchFaissGCPGenerative AiLangchainLanggraphLarge Language Models (Llms)LlamaindexLlmopsMicroservicesMlopsOpensearchPineconePrompt EngineeringPythonPyTorchRetrieval-Augmented Generation (Rag)Scikit-LearnSemantic KernelServerlessTensorFlowVector DatabasesWeaviate
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine



