Principal Machine Learning & AI Engineer
Quantum Search Partners
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Austin, TX
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Not Applicable
Posted March 13, 2026
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Responsibilities
Commitments
Responsibilities
- Feasibility Research: Conduct in-depth research to assess the technical and product feasibility of integrating new AI and machine learning advancements into core offerings.
- Idea Generation: Proactively generate, prototype, and validate innovative research ideas that can lead to next-generation features and products in fraud prevention, AML, IDV, and Device Intelligence.
- Algorithm Development: Design, implement, and experiment with advanced AI algorithms, including but not limited to deep learning, graph neural networks, reinforcement learning, and advanced statistical modeling.
- Collaboration: Work closely with the Product, Engineering, and Data Science teams to transition successful research prototypes into production-ready features.
- Knowledge Sharing: Disseminate research findings through internal presentations, technical reports, and potentially external publications.
- Embed AI in the Platform: Drive seamless integration of generative and traditional ML capabilities into core SaaS product, with a focus on real-time responsiveness and usability.
Commitments
Hybrid in Austin, TX
Not Met Priorities
What still needs stronger evidence
Requirements
- Experience: 5+years of post-doctoral or industry experience in AI research, preferably in a domain related to fraud detection, cybersecurity, financial technology, or risk management.
- Technical Expertise
- Deep expertise in multiple areas of AI/ML, such as Deep Learning, Time Series Analysis, Natural Language Processing, or Causal Inference.
- Proficiency in programming languages and frameworks commonly used in AI research (e.g., Python, PyTorch, TensorFlow).
- Demonstrated ability to formulate research questions, design experiments, and interpret complex results.
- Generative AI Experience: Solid understanding of LLM architecture, prompt engineering, embeddings, vector search (e.g., FAISS, pgvector, Milvus), and GenAI product patterns like RAG or tool use.
- Experience building AI/ML systems at scale, ideally in a SaaS, B2B or data-heavy product environment.
- Deep understanding of clustering, anomaly detection and other core Machine Learning algorithms
- Expertise with AI frameworks: Production level experience, and familiarity with AI frameworks such as LangChain, LangFuse, Guardrails, Haystack, or similar.
- Domain Knowledge: Strong understanding of the challenges and data unique to fraud detection, AML, IDV, or Device Intelligence is highly desirable.
- Problem-Solving: Proven track record of tackling highly ambiguous and complex research problems and delivering practical, high-impact solutions.
- System Design Strength: Ability to define architecture that balances latency, scale, experimentation, and cost — with a deep understanding of distributed systems.
- Mentoring and communication: Ability to clearly communicate and explain research results in written and spoken words.
- Proven track record of successful collaboration between software engineering and research teams to transfer research prototypes into production-ready features.
Preferred Skills
- Ph.D. in Computer science is preferred.
- Expertise with AI frameworks: Production level experience, and familiarity with AI frameworks such as LangChain, LangFuse, Guardrails, Haystack, or similar.
- Domain Knowledge: Strong understanding of the challenges and data unique to fraud detection, AML, IDV, or Device Intelligence is highly desirable.
- Cloud expertise: Preferably AWS cloud.
- Mentoring and communication: Ability to clearly communicate and explain research results in written and spoken words.
- Proven track record of successful collaboration between software engineering and research teams to transfer research prototypes into production-ready features.
Education
- (Not required) – Education: Masters in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- (Not required) – Ph.D. in Computer science is preferred.
- (Not required) – Experience: 5+years of post-doctoral or industry experience in AI research, preferably in a domain related to fraud detection, cybersecurity, financial technology, or risk management.
Fraud Prevention & AML Platform (Series-C)
$250,000-$300,000 base + Stock Options (Potential Flexibility)
Hybrid in Austin, TX
What You’ll Do
Feasibility Research: Conduct in-depth research to assess the technical and product feasibility of integrating new AI and machine learning advancements into core offerings.
Idea Generation: Proactively generate, prototype, and validate innovative research ideas that can lead to next-generation features and products in fraud prevention, AML, IDV, and Device Intelligence.
Algorithm Development: Design, implement, and experiment with advanced AI algorithms, including but not limited to deep learning, graph neural networks, reinforcement learning, and advanced statistical modeling.
Collaboration: Work closely with the Product, Engineering, and Data Science teams to transition successful research prototypes into production-ready features.
Knowledge Sharing: Disseminate research findings through internal presentations, technical reports, and potentially external publications.
Embed AI in the Platform: Drive seamless integration of generative and traditional ML capabilities into core SaaS product, with a focus on real-time responsiveness and usability.
What You Bring
Education: Masters in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. Ph.D. in Computer science is preferred.
Experience: 5+years of post-doctoral or industry experience in AI research, preferably in a domain related to fraud detection, cybersecurity, financial technology, or risk management.
Technical Expertise
Deep expertise in multiple areas of AI/ML, such as Deep Learning, Time Series Analysis, Natural Language Processing, or Causal Inference.
Proficiency in programming languages and frameworks commonly used in AI research (e.g., Python, PyTorch, TensorFlow).
Demonstrated ability to formulate research questions, design experiments, and interpret complex results.
Generative AI Experience: Solid understanding of LLM architecture, prompt engineering, embeddings, vector search (e.g., FAISS, pgvector, Milvus), and GenAI product patterns like RAG or tool use.
Experience building AI/ML systems at scale, ideally in a SaaS, B2B or data-heavy product environment. Deep understanding of clustering, anomaly detection and other core Machine Learning algorithms
Expertise with AI frameworks: Production level experience, and familiarity with AI frameworks such as LangChain, LangFuse, Guardrails, Haystack, or similar.
Domain Knowledge: Strong understanding of the challenges and data unique to fraud detection, AML, IDV, or Device Intelligence is highly desirable.
Cloud expertise: Preferably AWS cloud.
Problem-Solving: Proven track record of tackling highly ambiguous and complex research problems and delivering practical, high-impact solutions.
System Design Strength: Ability to define architecture that balances latency, scale, experimentation, and cost — with a deep understanding of distributed systems.
Mentoring and communication: Ability to clearly communicate and explain research results in written and spoken words. Proven track record of successful collaboration between software engineering and research teams to transfer research prototypes into production-ready features.
$250,000-$300,000 base + Stock Options (Potential Flexibility)
Hybrid in Austin, TX
What You’ll Do
Feasibility Research: Conduct in-depth research to assess the technical and product feasibility of integrating new AI and machine learning advancements into core offerings.
Idea Generation: Proactively generate, prototype, and validate innovative research ideas that can lead to next-generation features and products in fraud prevention, AML, IDV, and Device Intelligence.
Algorithm Development: Design, implement, and experiment with advanced AI algorithms, including but not limited to deep learning, graph neural networks, reinforcement learning, and advanced statistical modeling.
Collaboration: Work closely with the Product, Engineering, and Data Science teams to transition successful research prototypes into production-ready features.
Knowledge Sharing: Disseminate research findings through internal presentations, technical reports, and potentially external publications.
Embed AI in the Platform: Drive seamless integration of generative and traditional ML capabilities into core SaaS product, with a focus on real-time responsiveness and usability.
What You Bring
Education: Masters in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. Ph.D. in Computer science is preferred.
Experience: 5+years of post-doctoral or industry experience in AI research, preferably in a domain related to fraud detection, cybersecurity, financial technology, or risk management.
Technical Expertise
Deep expertise in multiple areas of AI/ML, such as Deep Learning, Time Series Analysis, Natural Language Processing, or Causal Inference.
Proficiency in programming languages and frameworks commonly used in AI research (e.g., Python, PyTorch, TensorFlow).
Demonstrated ability to formulate research questions, design experiments, and interpret complex results.
Generative AI Experience: Solid understanding of LLM architecture, prompt engineering, embeddings, vector search (e.g., FAISS, pgvector, Milvus), and GenAI product patterns like RAG or tool use.
Experience building AI/ML systems at scale, ideally in a SaaS, B2B or data-heavy product environment. Deep understanding of clustering, anomaly detection and other core Machine Learning algorithms
Expertise with AI frameworks: Production level experience, and familiarity with AI frameworks such as LangChain, LangFuse, Guardrails, Haystack, or similar.
Domain Knowledge: Strong understanding of the challenges and data unique to fraud detection, AML, IDV, or Device Intelligence is highly desirable.
Cloud expertise: Preferably AWS cloud.
Problem-Solving: Proven track record of tackling highly ambiguous and complex research problems and delivering practical, high-impact solutions.
System Design Strength: Ability to define architecture that balances latency, scale, experimentation, and cost — with a deep understanding of distributed systems.
Mentoring and communication: Ability to clearly communicate and explain research results in written and spoken words. Proven track record of successful collaboration between software engineering and research teams to transfer research prototypes into production-ready features.