Similar Jobs
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Design and deploy production-grade generative AI, NLP, LLM, RAG, and agentic AI solutions for enterprise healthcare applications. Build and fine-tune models, develop vector search pipelines, implement evaluation and safety controls, and productionize machine learning systems using cloud-native CI/CD, testing, monitoring, and observability practices. Collaborate with product, platform, data science, and healthcare teams to deliver scalable and responsible AI capabilities.
Top Skills:
Agentic AiAnthropic ClaudeAWSAzureCi/CdClaude CodeCodexCursorGCPGeminiGenerative AiHugging FaceLangchainLarge Language Models (Llms)LlamaLlamaindexMistralNatural Language Processing (Nlp)OpenaiPythonPyTorchRetrieval-Augmented Generation (Rag)Semantic KernelTensorFlowVector DatabasesVector SearchWindsurf
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.
Top Skills:
Agentic AiSparkAWSDatabricksEmbedding ModelsJavaKotlinLarge Language ModelsPythonRanking ModelsSQLTypescript
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
The Principal Machine Learning Engineer role focuses on developing and leading machine learning engineering initiatives at Atlassian. The provided description emphasizes Atlassian’s flexible work model, compensation, benefits, inclusion commitments, and employment verification requirements, but does not provide detailed technical responsibilities or qualifications.
Fully Remote Position
Preferred Locations: Midwest based: Indianapolis, IN or IL, Chicagoland Area preferred
Who We Are:
K1x is the leading data distribution platform for alternative investments. Simply put, our mission is to digitize the K-1 ecosystem. Our AI-powered K-1 extraction technologies surpass all other competition and we’re the first to produce a digital K-1. Learn more at www.k1x.io
About the role:
We are seeking a highly skilled and experienced Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in natural language processing (NLP) and extensive experience working with unstructured and semi-structured data such as financial statements and tax documents. As a Machine Learning Engineer, you will play a critical role in developing and implementing machine learning models that enhance our software’s ability to accurately and efficiently process partnership accounting and tax documents.
If you are an experienced Machine Learning Engineer or Data Scientist looking for an exciting opportunity to work on challenging problems and deliver machine learning products, we would love to hear from you. Join our team and help shape the future of alternative investments management and distribution!
- Develop and optimize machine learning models for parsing, extracting, and categorizing data in PDF tax documents.
- Collaborate with cross-functional teams to integrate machine learning solutions into our software products.
- Apply statistical analysis to identify signals, trends, and insights that can inform product development.
- Mentor and guide junior data scientists and team members, fostering a collaborative and innovative work environment.
Requirements
- Masters’ or PhD in Computer Science, Mathematics, Statistics, Data Science, or a related field
- 6+ years of relevant industry experience as a data scientist, with a focus on NLP/NLU projects
- Demonstrated experience leading end-to-end data science project implementations
- Excellent problem-solving skills with the ability to synthesize and communicate complex technical results to senior leaders and nontechnical audiences
- Proficiency in Python and a strong understanding of machine learning frameworks and libraries (e.g. scikit-learn, PyTorch, spaCy, Huggingface)
Qualifications:
- Masters’ or PhD in Computer Science, Mathematics, Statistics, Data Science, or a related field, or equivalent experience
- 6+ years of relevant industry experience as a data scientist, with a focus on NLP/NLU projects
- Demonstrated experience leading end-to-end data science project implementations
- Excellent problem-solving skills with the ability to synthesize and communicate complex technical results to senior leaders and nontechnical audiences
- Proficiency in Python and a strong understanding of machine learning frameworks and libraries (e.g. scikit-learn, PyTorch, spaCy)
Preferred experience:
- Previous experience with applications of NLP to financial documents
- Familiarity with alternative investment accounting needs
- Experience deploying machine learning models using containerization and orchestration technologies
Benefits
- Unlimited Vacation Policy + Sick Time + Holidays
- Paid Parental Leave
- Fully Remote Opportunity
- Healthcare Benefits and 401K
- Growing Startup Culture
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
