The role focuses on the research, prototyping, evaluation, and improvement of AI solutions, with hands-on work across LLM-based systems, including agent-style workflows and retrieval-augmented generation (RAG) where appropriate. You will work end-to-end: collaborating with stakeholders and product managers to define problems, building and validating prototypes, presenting findings to diverse audiences, and supporting engineering teams during implementation and production rollout.

Key Responsibilities:

End-to-end AI solution ownership

• Own AI projects or functional modules from problem definition through prototype validation and production support.

• Partner with product managers and business stakeholders to translate real-world problems into clearly scoped data science and AI initiatives.

• Independently plan and execute research, experimentation, and iteration cycles in ambiguous problem spaces.

• Design AI solutions with a system-level perspective, ensuring scalability, maintainability, and long-term sustainability.

Applied AI, LLMs, and agentic systems

• Design and prototype LLM-powered solutions, including RAG-based systems and agent-like workflows (e.g. tool use, orchestration, multi-step reasoning).

• Contribute to defining system behavior, scope, and constraints, with attention to quality, robustness, and operational considerations.

• Stay current with emerging AI techniques and apply them pragmatically to solve business problems.

Evaluation, validation, and performance improvement

• Build and maintain evaluation frameworks to assess AI system performance (accuracy, reliability, relevance, robustness, safety).

• Develop quantitative and qualitative metrics, benchmarks, and testing approaches to validate prototypes and track improvements.

• Analyze existing solutions to identify gaps and drive continuous, data-driven performance enhancements.

Collaboration and communication

• Work closely with data scientists, engineers, and product teams to ensure smooth transition from prototype to production.

• Clearly communicate methods, assumptions, results, and limitations to technical and non-technical audiences.

• Support engineering teams during implementation by clarifying evaluation criteria, edge cases, and expected system behavior.

• Serve as a technical authority and actively mentor junior data scientists, shaping best practices in experimentation, evaluation, and AI system design.

Ways of working

• Contribute actively within an Agile / SCRUM development environment.

• Apply good engineering hygiene in research and prototype code to enable reproducibility and collaboration.

Required Qualifications

• 6+ years of experience in data science, applied machine learning, or a closely related role.

• Strong mathematical, statistical, and machine learning foundations, including probability, statistics, optimization, and model evaluation.

• Proven ability to select, apply, and critically evaluate ML models and algorithms for real-world problems.

• Strong Python skills for analysis, modelling, experimentation, and prototyping.

• Strong SQL skills for data exploration, transformation, and analytical workflows.

• Excellent analytical thinking and problem-structuring abilities; comfort operating independently with loosely defined goals.

• Experience using Git for version control and collaborative development.

• Strong English communication skills, both written and verbal.

Preferred Qualifications (Strong Plus)

• Hands-on experience with LLMs, including prompt/system design and building real-world applications.

• Experience with RAG systems, including retrieval strategies, chunking, evaluation, and performance tuning.

• Experience designing or contributing to agent-style AI systems and familiarity with concepts such as agent evaluation, guardrails, and reliability testing.

• ML modeling experience (e.g. supervised learning, ranking, classification) beyond exploratory analysis.

• Understanding of software engineering best practices, including testing strategies and CI/CD concepts.

• Experience working in Azure or similar cloud environments.

• Familiarity with Snowflake (and optionally Snowflake AI) as part of a modern data stack.

• Experience collaborating closely with domain experts; financial domain exposure is a plus but not required for strong technical candidates.

What Success Looks Like in This Role

• You independently deliver high-quality AI prototypes and evaluation frameworks for a defined subdomain or application module.

• You proactively define scope, success metrics, and experimentation plans, helping shape architecture and design decisions.

• Your evaluation approaches enable reliable comparison, regression prevention, and continuous improvement of AI solutions.

• Engineering teams can confidently productionize your work thanks to clear designs, metrics, and collaboration.

• Over time, you raise the technical maturity of AI development and evaluation practices within the team.

Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

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We are hiring a Machine Learning Engineer to join our Localization Data & AI team, reporting to the Localization Data & AI Manager. The Loc Data & AI team's mission is to empower EA Localization through intelligent, data-driven solutions,building scalable AI systems, streamlining ML operations, and creating tools that enhance the quality and efficiency of localized content.

This role focuses on designing, deploying, and maintaining ML models and infrastructure, collaborating closely with Data Engineers and Data Scientists.

Responsibilities

• Design, build, and maintain scalable and production-ready ML pipelines to support AI-driven localization workflows.
• Collaborate with cross-functional teams to understand business needs and translate them into ML solutions.
• Train, evaluate, and fine-tune models for NLP, Computer Vision, and other ML use cases.
• Deploy and monitor ML models in different environments, ensuring performance, scalability, and reliability.
• Develop preprocessing pipelines tailored to ML/DL tasks by working with large structured and unstructured datasets in multiple languages.
• Leverage MLOps best practices for versioning, testing, CI/CD, and monitoring of models (e.g., MLflow, Sagemaker, or VertexAI).
• Design, develop, and maintain API REST services using languages such as Python, .NET, and/or Node.js.
• Partner with Data Engineers and Data Scientists to ensure efficient data access and optimized feature engineering processes.
• Contribute to continuous model and system improvement through experiment tracking, feedback loops, and performance analysis.
• Conduct code reviews and ensure high-quality coding standards.
• Optimize applications for maximum speed and scalability.
• Collaborate with cross-functional teams to define, design, and ship new features.
• Ensure adherence to ethical AI and data governance standards.

Qualifications

• 2+ years of hands-on experience in Machine Learning Engineering.
• Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or related discipline.
• Strong Python programming skills, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face).
• Proficiency in building and deploying ML models in real-world applications.
• Familiarity with data processing frameworks (Pandas, NumPy) and orchestration tools (Airflow, Prefect).
• Solid understanding of model lifecycle management and MLOps tools (e.g., MLflow, VertexAI, SageMaker, AzureML).
• Experience working with APIs, RESTful services, and microservice-based architecture.
• Knowledge of NLP and Computer vision techniques and tools for multilingual data is a strong plus.
• Experience with cloud services (AWS, Azure, or GCP) for ML/DL development and deployment.
• Experience with WebAPI and RESTful services.
• Knowledge of software engineering best practices and tools (Gitlab and Github), such as Continuous Integration and Version Control (Git).
• Oversee and contribute to the underlying infrastructure that powers ML systems (e.g, Terraform) ensuring robust, maintainable, and secure foundations for scalable deployment.
• Strong debugging skills and fluent in reading code.
• Strong problem-solving skills, and ability to communicate technical concepts clearly with stakeholders.
• Excellent communication and collaboration skills, with the ability to translate data insights into business impact.

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Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organisations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence.

This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.

About the Team

The Professional Services R&D team is a new, dynamic group at the forefront of innovation within Okta. Our mission is to design and build reusable, scalable assets and tools that empower our delivery teams and partners. By making customer engagements more efficient, streamlined, and cost-effective, we directly contribute to our customers' success and accelerate their time-to-value with Okta. This is a unique opportunity to join a strategic team from the ground up and shape the future of Okta's professional services.

Position Summary

As a Senior Software Engineer on the R&D team, you will be a technical leader and a significant individual contributor. You will take ownership of complex projects from design to completion, influencing the technical direction of the assets we build. Beyond writing code, you will be a mentor to other engineers, a champion for code quality, and a key partner to the Architect and Product Manager. We are looking for an experienced and passionate engineer who can tackle ambiguous problems, make critical design decisions, and elevate the entire team's technical capabilities.

Responsibilities

• Lead the design, development, and deployment of large-scale, complex software assets across the full technology stack (Java, React, Node, Python, .NET).
• Take ownership of major features and initiatives, driving them from technical specification through to delivery.
• Mentor and coach other engineers on the team, fostering their growth through code reviews, design discussions, and pair programming.
• Partner with the team's Architect to translate architectural vision into tangible, high-quality code and system designs.
• Drive engineering best practices in code quality, testing, performance, and scalability.
• Identify and advocate for improvements to our technology stack, development processes, and overall system architecture.
• Act as a subject matter expert in one or more technical domains, providing guidance and expertise to the rest of the organisation.

Qualifications

• Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
• 5+ years of professional software development experience, with a proven track record of delivering complex, high-impact projects.
• Deep expertise in one or more of the following programming languages: Java, Python, Node.js, or C# (.NET).
• Strong experience with modern front-end frameworks such as React.
• Demonstrated experience in system design and architecture, with the ability to make and justify technical trade-offs.
• Proven experience mentoring junior engineers and leading technical projects.
• Solid understanding of cloud platforms (AWS, Azure, GCP), CI/CD pipelines, and software engineering best practices.
• Excellent problem-solving skills and the ability to navigate ambiguity.
• Strong communication and collaboration skills, with a history of effective cross-functional partnership.

#LI-Onsite

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The Okta Experience

• Supporting Your Well-Being
• Driving Social Impact
• Developing Talent and Fostering Connection + Community

We are intentional about connection. Our global community, spanning over 20 offices worldwide, is united by a drive to innovate. Your journey begins with an immersive, in-person onboarding experience designed to accelerate your impact and connect you to our mission and team from day one.

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Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you'll use to improve your product's performance. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will:

• Own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems
• Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
• Have experience gathering business requirements and translating them into technical solutions
• Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
• Push production code in multiple development environments, writing and debugging code directly in both our customer's and Scale's codebases.

Ideally you'd have:

• 7+ years of full-time engineering experience, post-graduation
• A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client’s business goals
• Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.
• Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
• Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)

Nice to haves:

• Strong knowledge of software engineering best practices
• Have built applications taking advantage of Generative AI in real, production use cases
• Familiarity with state of the art LLMs and their strengths/weaknesses

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At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment.

As a Staff Software Engineer, you will define and drive both the architectural roadmap and implementation of core platforms and software systems. You will be responsible for providing high-level vision and driving adoption across the engineering org for orchestration, data abstraction, data pipelines, identity & access management, and underlying cloud infrastructure.

Impact and Responsibilities:

• Architectural Vision: You will drive the design and implementation of foundational systems, acting as a bridge between high-level business goals and technical goals.

• Cross-Functional Leadership: You will collaborate with cross-functional teams to define and drive adoption of the next generation of features for our AI data infrastructure.

• Technical Ownership: You are responsible for proactively identifying and driving opportunities for organizational growth, driving improvements in programming practices, and upgrading the tools that define our development lifecycle.

• Technical Mentorship: You will serve as a subject matter expert, presenting technical information to stakeholders and providing the guidance to elevate the engineering culture across the company.

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialities in back-end systems.

• Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).

• Demonstrated a track record of independent ownership and leadership across successful multi-team engineering projects.

• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.

• Experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc.

• Experience with orchestration platforms, such as Temporal and AWS Step Functions.

• Experience with NoSQL document databases (MongoDB) and structured databases (Postgres).

• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI, ArgoCD).

Nice to haves:

• Experience with data warehouses (Snowflake, Firebolt) and data pipeline/ETL tools (Dagster, dbt).

• Experience scaling products at hyper-growth startups.

• Excitement to work with AI technologies.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $252,000-$315,000 USD

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You will drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. You'll collaborate with cross-functional teams to define, design, and deliver new features. You'll also proactively identify opportunities for, and drive improvements to, current programming practices, including process enhancements and tool upgrades.

Ideally, you'd have 3+ years of full-time engineering experience, post-graduation with specialities in back-end systems. You should have extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred). You should show a track record of independent ownership of successful engineering projects. You should possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.

You should have experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc. You should have experience with orchestration platforms, such as Temporal and AWS Step Functions. You should have experience with NoSQL document databases (MongoDB) and structured databases (Postgres). You should have strong knowledge of software engineering best practices and CI/CD tooling (CircleCI).

Nice to haves include experience with data warehouses (Snowflake, Firebolt) and data pipeline/ETL tools (Dagster, dbt). Experience with authentication/authorization systems (Zanzibar, Authz, etc.) is also a plus. Experience scaling products at hyper-growth startups is highly valued. Excitement to work with AI technologies is a must.

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Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you'll use to improve your product's performance. You'll own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems, leveraging our Scale Generative Platform (SGP) to build the most advanced AI agents across the industry.

Responsibilities:

• Own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems
• Leverage SGP to build the most advanced AI agents across the industry including multimodal functionality, tool-calling, and more
• Have experience gathering business requirements and translating them into technical solutions
• Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
• Push production code in multiple development environments, writing and debugging code directly in both our customer's and Scale's codebases.

Ideal candidate will have a love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client's business goals, a strong engineering background, deep familiarity with a data-driven approach when iterating on machine learning models, and experience working with cloud technology stack and developing machine learning models in a cloud environment.

Nice to have: strong knowledge of software engineering best practices, experience building applications taking advantage of Generative AI in real, production use cases, and familiarity with state of the art LLMs and their strengths/weaknesses.

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Daily data-driven experiments will provide key insights around model strengths and inefficiencies which you'll use to improve your product's performance. You'll own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems, leveraging our Scale Generative Platform (SGP) to build the most advanced AI agents across the industry.

Responsibilities:

• Own, plan, and optimize the AI behind our Enterprise customer's deepest technical problems
• Leverage SGP to build the most advanced AI agents across the industry, including multimodal functionality, tool-calling, and more
• Have experience gathering business requirements and translating them into technical solutions
• Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needs
• Push production code in multiple development environments, writing and debugging code directly in both our customer's and Scale's codebases

Ideally, you'd have:

• A love for solving deeply complex technical problems with ambiguity using state of the art research and AI to accomplish your client's business goals
• Strong engineering background: a Bachelor’s degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background
• Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model results
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
• Proficiency in Python to write, test and debug code using common libraries (ie numpy, pandas)

Nice to haves:

• Strong knowledge of software engineering best practices
• Have built applications taking advantage of Generative AI in real, production use cases
• Familiarity with state of the art LLMs and their strengths/weaknesses

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You will help build a worldwide dataset for our exploration program, with careful attention to identifying and quantifying uncertainty in the data and predictions.

You will create models and develop software to accelerate discovery of critical battery metals.

You will join an outstanding team of data scientists and engineers and work closely with (*applicant's) world-renowned geoscientists to incorporate our best understanding of the chemical and physical processes that create ore deposits.

Working with your geoscience colleagues, you will create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce uncertainty in our predictions and guide the next phase of field work.

Ultimately, your role is to help KoBold make valuable discoveries by building data tools to solve scientific problems.

As one of the early members of this team, you will help build these tools from the ground up.

Responsibilities:

• Help develop KoBold's proprietary software exploration tools.
• Find and curate geophysical, geochemical, geologic, and geographic data and integrate it into KoBold's proprietary data system.
• Build models to make statistically valid predictions about the locations of compositional anomalies within the Earth's crust.
• Create effective visualizations for evaluating model performance and enabling rapid interaction with the underlying data and key features.
• Develop and apply data processing, statistical, and physics-based techniques to geoscientific data , from computer vision to geophysical inversions , and use the results to guide our targeting efforts and inform our acquisition and exploration decisions.
• Present to and collaborate with our external partners and stakeholders.

Qualifications:

• Technical skills, including extensive experience with Python's data science packages and general software engineering practices.
• Collaborative software development (git), and familiarity with software engineering best practices like unit test / integration test suites, and CICD pipelines.
• Cloud computing resources.
• Building predictive models, applying them to different problems, and evaluating and interpreting the results.
• Data from a variety of physical systems.
• Geospatial analyses and visualizations.

Technical knowledge:

• Broad skills in and knowledge of applied statistics and Bayesian inference.
• Substantial understanding of machine learning algorithms.

Training and work experience:

• An advanced degree in the physical sciences, engineering, computer science, or mathematics.
• A minimum work experience of 4 years post PhD or 8 years post MS, ideally as a data scientist or data engineer.
• Experience leading technical teams to apply novel scientific approaches to core business problems.

Work practices and motivation:

• Ability to take ownership and responsibility of large projects.
• Ability to explain technical problems to and collaborate on solutions with domain experts.
• Communicates well on a collaborative, cross-functional team.
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.
• Ability to independently prioritize multiple tasks effectively.
• Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain.
• Enjoys constantly learning such that you are driving insights through using our tools in exploration and willing to work directly with geologists in the field.
• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.
• A valid passport and willingness to travel to observe our work at Mingomba or at an exploration site around the world.

Preferred skills include creating machine learning models on geospatial data, geostatistics, image processing or computer vision, and distributed computing applications for machine learning and other computations.

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Our full-stack antibody drug development platform uses machine learning to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models.

As a successful candidate, you will apply your world-class machine learning skillset to refine and expand this state-of-the-art protein engineering platform. Success will mean not only hands-on methods development, but helping shape the direction for future machine learning research, and actively participating in the application of our platform to the accelerated design of new therapeutics.

Key Responsibilities

• Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
• Provide leadership, technical guidance, and mentorship to other machine learning and data science FTEs and interns.
• Help set strategy for future machine learning research, driven by a strong high-level understanding of BigHat programs and operations as well as real-world drug development challenges.
• Develop, refine, and deploy de novo design methods for generating initial hits to challenging, therapeutically interesting targets.
• Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
• Maintain an in-depth understanding of the current state-of-the-art in machine learning-driven protein engineering, both in the literature and at BigHat.
• Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.
• Provide machine learning expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
• Collaborate with our engineering team to ensure maximal efficiency in the automated and agentic deployment of our latest models to our therapeutics programs.
• Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize machine learning methods development accordingly.

Skills, Knowledge & Expertise

• PhD in machine learning/computer science or in the hard sciences with 5+ years experience post-graduation in developing and applying novel machine learning methods, and a strong quantitative background.
• Publications in major machine learning conferences and/or leading journals, and an extensive demonstrable track record developing and applying novel machine learning in industry.
• Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
• Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
• Enjoys a fast-paced environment and excels at executing across multiple projects.
• Familiarity with the current state-of-the-art in machine learning-driven protein engineering
• Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, and experience training and deploying models on AWS.

Benefits

The salary estimated for this position is $254,000 - $290,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.

What BigHat Offers:

• Range of health insurance plan options through Anthem and Kaiser (monthly credit if benefit waived)
• Dental, and vision coverage through Guardian
• Additional well-being benefits through Nayya, OneMedical, Wagmo, Rula, and more
• 401(k) with company match
• DTO, two weeks of company-wide shutdown, and 12 company holidays
• Paid parental leave

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As a vital member of the Compliance team, you will have the opportunity to support the design, implementation, and optimization of compliance programs across all applicable Anchorage Digital legal entities.

You will work closely with various compliance functions, particularly Financial Crimes Compliance, to drive efficiency and effectiveness within the program.

Your expertise will be critical in transforming raw data into actionable insights, driving process improvements, and leveraging technology to enhance our overall compliance posture.

This role is ideal for a proactive and 'young and hungry' technologist who thrives on solving complex problems in a dynamic regulatory environment.

You'll gain deep exposure to diverse compliance domains and have the chance to apply your data expertise to strengthen Anchorage Digital's global compliance function through analytics, automation, and the strategic use of AI tools.

It is important that you are well-organized, have a strong analytical background, can effectively manage competing priorities, and can adapt to rapid change in a fast-paced environment.

If you thrive under uncertainty and are motivated to excel in a dynamic environment with competing priorities, this role is designed for you.

Anchorage Digital values individuals who are proactive, detail-oriented, and innovative.

Technical Skills:

• Expert in Compliance-related data tables and models, understanding the nuances of the data, the underlying codes, and the limitations of the models.

• Work with stakeholders to drive the automation of key compliance processes and workflows using internal tools, such as Know-Your-Customer, sanctions screening, suspicious activity identification and reporting to improve efficiency and reduce manual effort.

• Experience experimenting with and deploying AI solutions.

Complexity and Impact of Work:

• Support the development and enhancement of BSA/AML models, including transaction monitoring, sanctions screening, customer risk rating, blockchain analytics, and other relevant BSA/AML tools.

• Contribute to process improvements and best practices for the FCC Analytics team, including code review, testing frameworks, project management, and documentation.

• Conduct ad hoc analyses and respond to time-sensitive data requests from auditors, regulators, or other Compliance teams with accuracy and speed.

Organizational Knowledge:

• Develop deep understanding of Anchorage Digital's business model across custody, staking, stablecoins, and trading, and how data flows through these domains and the compliance models.

• Coordinate with cross-functional teams (Product, Engineering, etc.) to implement and improve tools and processes within the broader Compliance department.

• Understand how the Compliance and FCC team fits within the broader organizational structure, align work with team and company priorities.

Communication and Influence:

• Manage competing priorities across strategic projects and urgent ad-hoc requests; ask clarifying questions to scope ambiguous requests and push back constructively when requirements are unclear, proposing alternative approaches.

• Present findings and data insights with appropriate context, visual aids, and tailored communication style

You may be a fit for this role if you have:

• Experience: 2–3 years of experience in a data analytics or data science role, with a strong understanding of blockchain, cryptocurrency, and the financial services industry.

• Technical Proficiency: Demonstrated ability to operate autonomously to manage multiple competing priorities.

• Automation & AI Aptitude: Experience with or a strong interest in automation principles, leveraging existing AI tools, and exploring new AI tools (such as AI Agents) to enhance productivity.

• Technologist Mindset: While not necessarily an engineer, a strong understanding of how systems, data flows, and technologies interact is essential.

• Eagerness to learn and apply new technologies.

Although not a requirement, bonus points if:

• Prior experience in the financial service industry, crypto industry, start-up, or a fast-paced, evolving environment where you've worn multiple hats and adapted quickly;

• General understanding of regulatory compliance, financial crimes, and risk management.

• Experience with data transformation tools like dbt or similar; familiarity with version control (Git) and software engineering best practices for analytics

• Python or other scripting languages for data analysis, automation, or data pipeline work

• Excellent communication skills with experience presenting technical findings to both technical and non-technical stakeholders

• Proven ability to manage competing priorities and deliver accurate results under tight deadlines, especially for compliance or audit-related requests

• Detail-oriented and quality-focused with a commitment to data accuracy, testing, and documentation

• You were emotionally moved by the soundtrack to Hamilton, which chronicles the founding of a new financial system.

Additional Information About Anchorage Digital:

Who we are

The Anchorage Village, what we call our team, brings together the brightest minds from platform security, financial services, and distributed ledger technology.

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