Responsibilities:

• Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
• Optimize encoding techniques to improve model training efficiency and performance
• Collaborate closely with research teams to understand their evolving needs around data representation
• Build infrastructure that enables researchers to experiment with novel tokenization approaches
• Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
• Create robust testing frameworks to validate tokenization systems across diverse languages and data types
• Identify and address bottlenecks in data processing pipelines related to tokenization
• Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams

You May Be a Good Fit If You:

• Have significant software engineering experience with demonstrated machine learning expertise
• Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
• Can work independently while maintaining strong collaboration with cross-functional teams
• Are results-oriented, with a bias towards flexibility and impact
• Have experience with machine learning systems, data pipelines, or ML infrastructure
• Are proficient in Python and familiar with modern ML development practices
• Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With:

• Working with machine learning data processing pipelines
• Building or optimizing data encodings for ML applications
• Implementing or working with BPE, WordPiece, or other tokenization algorithms
• Performance optimization of ML data processing systems
• Multi-language tokenization challenges and solutions
• Research environments where engineering directly enables scientific progress
• Distributed systems and parallel computing for ML workflows
• Large language models or other transformer-based architectures (not required)

The annual compensation range for this role is $320,000-$405,000 USD.

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This role sits at the intersection of traditional financial plumbing and programmable digital money , partnering directly with major U.S. banks and ecosystem partners while shaping the core primitives and operational processes that power regulated stablecoin issuance.

Key Responsibilities:

• Drive Technical Sales & Implementation with Banks (25%): Partner with the largest U.S. banks and financial institutions to understand their needs and implement stablecoin solutions for treasury management, cross-border settlement, client offerings, and more.
• Build & Strengthen the Partner Ecosystem (25%): Collaborate with exchanges, card networks, payment companies, foreign banks, asset managers, and other key players to deliver best-in-class features and co-develop capabilities that make our stablecoins the preferred infrastructure.
• Ensure Institutional-Grade Compliance & Operations (25%): Lead cross-functional processes with compliance, regulatory, finance, accounting, treasury management, operations, audit, and other teams. Maintain the highest regulatory standards while enabling speed and innovation.
• Work Hand-in-Hand with Engineering (25%): Define and prioritize core product features by deeply scoping problems, articulating constraints, and co-creating solutions with engineering.

Technical Skills:

• Define stablecoin issuance product strategy, primitives, and roadmap across onboarding, mint/burn, controls, reporting/attestations, and integrations
• Translate complex regulatory + technical constraints into clear product requirements and partner-ready implementations
• Lead multi-stakeholder programs across compliance, regulatory, finance/accounting, treasury, operations, audit, BD, and engineering in a high-stakes environment
• Drive outcomes that scale issuance volume, expand partner ecosystems, and improve time-to-implement while maintaining institutional-grade controls
• Navigate OCC/banking-grade governance, auditability expectations, and risk management practices to enable responsible innovation
• Build strong cross-functional operating rhythms and decision-making frameworks that keep delivery moving without compromising controls
• Influence senior bank executives and internal leaders through crisp narratives, tradeoff framing, and execution clarity
• Align technical, operational, and commercial stakeholders on sequencing, launch readiness, and success metrics

Requirements:

• Track record shipping complex fintech/payments/banking products with meaningful partner and regulatory dependencies
• Proven ability to run end-to-end cross-functional programs in regulated environments (risk, controls, audit, policy, compliance)
• Strong stakeholder management with executive-level customers/partners and deep technical counterparts
• Bias for ownership and execution in ambiguous, fast-moving environments

Bonus Points:

• Direct experience with stablecoin issuance, tokenization, or institutional crypto infrastructure
• Experience building platform products and partner ecosystems (APIs, integrations, co-development)
• Prior work in or with federally regulated financial institutions (bank controls/governance)

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We are seeking an experienced Machine Learning Systems Engineer to join our Encodings and Tokenization team at Anthropic. This cross-functional role will be instrumental in developing and optimising the encodings and tokenization systems used throughout our Finetuning workflows. As a bridge between our Pretraining and Finetuning teams, you'll build critical infrastructure that directly impacts how our models learn from and interpret data. Your work will be foundational to Anthropic's research progress, enabling more efficient and effective training of our AI systems while ensuring they remain reliable, interpretable, and steerable.

Responsibilities:

• Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
• Optimise encoding techniques to improve model training efficiency and performance
• Collaborate closely with research teams to understand their evolving needs around data representation
• Build infrastructure that enables researchers to experiment with novel tokenization approaches
• Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
• Create robust testing frameworks to validate tokenization systems across diverse languages and data types
• Identify and address bottlenecks in data processing pipelines related to tokenization
• Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams

You May Be a Good Fit If You:

• Have significant software engineering experience with demonstrated machine learning expertise
• Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
• Can work independently while maintaining strong collaboration with cross-functional teams
• Are results-oriented, with a bias towards flexibility and impact
• Have experience with machine learning systems, data pipelines, or ML infrastructure
• Are proficient in Python and familiar with modern ML development practices
• Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With:

• Working with machine learning data processing pipelines
• Building or optimising data encodings for ML applications
• Implementing or working with BPE, WordPiece, or other tokenization algorithms
• Performance optimisation of ML data processing systems
• Multi-language tokenisation challenges and solutions
• Research environments where engineering directly enables scientific progress
• Distributed systems and parallel computing for ML workflows
• Large language models or other transformer-based architectures (not required)

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles.

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This is a Product Engineering role specialized in monetization systems, and will lead that space at Replit. It’s a direct line to business impact. But it’s also critical to get right for our users. These are some of the most critical user journeys to get right. Getting them wrong creates the most frustrating experiences for users. So we’re looking for engineers who can build reliable and scalable billing systems and abstractions, while also translating that to an intuitive and friendly user experience.

You will:

• Lead the architecture and implementation of monetization systems at Replit.
• Create seamless payment experiences for users for both product-led and sales-led motions.
• Build new abstractions and APIs for other engineers at Replit to monetize their new products.
• Iterate on pricing and packaging tactics to drive revenue growth. Examples include coupon codes and referral systems.
• Create monitoring and feedback systems so that we can proactively spot problems, fix them, and optimize performance.

Required skills and experience:

• 6+ years of engineering experience, with strong skills working on the backend.
• Direct working experience in at least one of the following:
• Subscription platforms
• Usage-based billing
• SaaS Taxation
• Payment platforms
• Tokenization
• Self-directed and comfortable working autonomously in ambiguous environments.
• Excellent problem-solving skills with ability to debug complex billing issues and edge cases.
• Experience implementing customer-facing billing interfaces that simplify complex pricing structures.

_Tools + Tech Stack_ for this role:

• Python, TypeScript, React, Postgres, GraphQL, and Nodejs.

_Bonus Points_ :

• Experience working with Orb, Metronome, or Stripe usage based billing.
• Experience with coupons, referral programs, or other monetization growth tactics.

Full-Time Employee Benefits Include:

• Competitive Salary & Equity
• 401(k) Program with a 4% match
• Health, Dental, Vision and Life Insurance
• Short Term and Long Term Disability
• Paid Parental, Medical, Caregiver Leave
• Commuter Benefits
• Monthly Wellness Stipend
• Autonomous Work Environment
• In Office Set-Up Reimbursement
• Flexible Time Off (FTO) + Holidays
• Quarterly Team Gatherings
• In Office Amenities

Want to learn more about what we are up to?

• Meet the Replit Agent
• Replit: Make an app for that
• Replit Blog
• Amjad TED Talk

Interviewing + Culture at Replit

• Operating Principles
• Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Compensation Range: $265K - $340K

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This is a Principal Product Engineering role focused on Money Infrastructure at Replit. You'll work on the financial backbone that powers how Replit earns money, how builders earn money, and how Agents transact.

This role sits at the intersection of engineering, product, and the business. The systems you build directly impact revenue, trust, and some of the most critical user journeys on the platform. Getting them right enables growth, experimentation, and global scale. Getting them wrong creates broken payments, confusing pricing, and lost trust.

We're looking for engineers who can design and scale reliable financial systems while translating complex monetary logic into intuitive, user-friendly experiences for both Replit customers and builders on the platform. We love folks who have a passion for monetizing innovation and being a part of the greater pricing story.

Responsibilities:

• Lead the design, architecture, and implementation of Replit's core money infrastructure, spanning pricing, billing, payments, and monetization.
• Own and scale the global order-to-cash foundation supporting credit-based subscriptions, usage-based billing, marketplaces, in-app payments, and commerce for Agents.
• Enable rapid pricing and packaging experimentation across the company by building flexible abstractions and APIs for new SKUs, plans, and monetization models.
• Build high-converting, localized payment experiences across geographies — thinking globally while enabling users to pay locally.
• Power builder monetization by creating payment rails for apps, Agents, subscriptions, and new monetization primitives.
• Partner closely on data specifications with finance, accounting, and data teams to produce accurate, auditable, and reliable financial data.
• Collaborate with external monetization and payments partners (e.g. billing platforms, payment processors) to accelerate execution.
• Create monitoring, observability, and feedback systems to proactively detect issues, resolve failures, and optimize performance across financial flows.

Required Skills and Experience:

• 8+ years of professional software engineering experience, with strong backend expertise.
• Hands-on experience building or operating at least one of the following:
• Subscription or recurring billing platforms
• Usage-based or metered billing systems
• Payment processing platforms
• SaaS taxation or compliance systems
• Tokenization or credits-based systems
• Strong system design skills, especially for high-reliability, high-correctness domains like money.
• Comfort working autonomously in ambiguous, fast-moving environments with high business impact.
• Excellent debugging and problem-solving skills, including handling complex billing edge cases and failure modes.
• Experience building customer-facing billing or payments interfaces that simplify complex pricing models.

Tools + Tech Stack for this role:

• Rust
• TypeScript
• React
• Postgres
• GraphQL
• Node.js

Bonus points:

• Experience with usage-based billing platforms like Orb, Metronome, or Stripe Billing.
• Experience building coupons, referral programs, credits, or other monetization growth systems.
• Experience working on marketplaces, fintech integrations, or multi-party payment flows.
• Prior work on globally distributed payment systems with localization and compliance considerations.

Full-Time Employee Benefits Include:

• Competitive Salary & Equity
• 401(k) Program with a 4% match
• Health, Dental, Vision and Life Insurance
• Short Term and Long Term Disability
• Paid Parental, Medical, Caregiver Leave
• Commuter Benefits
• Monthly Wellness Stipend
• Autonomous Work Environment
• In Office Set-Up Reimbursement
• Flexible Time Off (FTO) + Holidays
• Quarterly Team Gatherings
• In Office Amenities

Want to learn more about what we are up to?

• Meet the Replit Agent
• Replit: Make an app for that
• Replit Blog
• Amjad TED Talk

Interviewing + Culture at Replit

• Operating Principles
• Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

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Product & Solutions Lead, Safety and Security

Location

San Francisco

Employment Type

Full time

Department

Intelligence & Investigations

Compensation

• $288K – $425K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Intelligence & Investigations (I2) team detects and disrupts abuse and strategic risks so people can use AI safely. We translate real-world signals, investigations, and external threat intelligence into practical mitigations, operating guidance, and partner-ready support that improves safety outcomes across the AI ecosystem.

About the Role

As a Product & Solutions Lead focused on safety and security, you will build and operate 0–1 products, services, and technical solution packages that help developers and public institutions move from experimentation to durable, trusted outcomes—while maintaining public safety, transparency, and respect for privacy and rights.

This role balances two modes of delivery:

1. Bespoke products and technical solutions for strategic internal and external partners, and

1. Scalable product and solution packages that can be reused broadly across partners and deployments.

Training is a component of scale, but not the center of gravity. You will also ship reference implementations, playbooks, evaluation kits, and repeatable operating models that partners can adopt and operate.

You will work directly with engineers and a multidisciplinary group of safety and geopolitical analysts, and data and quantitative scientists to convert complex, evolving challenges into solutions that teams can adopt in high-stakes environments.

This role is based in San Francisco, CA (hybrid, 3 days/week). Relocation support is available.

In this role, you will:

• Own the 0–1 roadmap for safety and security solution offerings: define the target users, problem statements, tools, operating models, success metrics, and the set of reusable deliverables we ship.

• Design and ship bespoke technical solutions for priority partners (internal and external), then abstract what works into reusable patterns and toolkits.

• Build partner-ready technical artifacts: solution blueprints, reference architectures, evaluation and monitoring guidance, incident/response playbooks, and deployment checklists.

• Package open-source and proprietary capabilities into adoption-ready solutions (e.g., reference implementations, configuration patterns, validated workflows).

• Maintain a consistent delivery model across engagements: intake, scoping, governance alignment, execution cadence, and retrospectives that improve the offering over time.

• Translate evolving threats into actionable guidance and updates for solution packages (e.g., scams/fraud patterns, cyber-enabled threats, ecosystem abuse trends).

• Develop lightweight enablement components as needed: targeted technical modules, hands-on labs, and readiness assessments that accelerate adoption of the solutions.

• Define and instrument impact measurement: adoption milestones, readiness indicators, reliability and safety posture improvements, and partner satisfaction with outputs.

• Partner closely across engineering, safety, geopolitical analysis, and quantitative teams to ensure solutions are technically credible, threat-informed, and measurable.

• Communicate crisply and decision-readily to internal and external stakeholders: progress, trade-offs, risks, and recommendations.

You might thrive in this role if you:

• Have 6+ years in product, technical program leadership, solutions, or platform operations, especially in safety, security, risk, integrity, or enterprise/public-sector contexts.

• Have built 0–1 solution offerings (product plus services or productized services): taking ambiguous needs, shipping something concrete, then scaling it into a repeatable model.

• Have a builder’s mindset: comfortable incubating early-stage ideas, testing them with partners, and evolving them into durable, repeatable safety and security solutions.

• Can go deep with engineers and still produce partner-ready artifacts that are clear

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About the role

As a Research Engineer you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting-edge challenges in the Generative AI space, with a focus on creating high-quality, expressive and real-time synthetic voices. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses.

Responsibilities

• Adapt models for new conditioning inputs (emotion, speed, prosody, speaker control, etc.).
• Fine-tune and optimize speech models using advanced techniques such as DPO (Direct Preference Optimization), LoRA, and other parameter-efficient methods to improve voice quality and expressiveness.
• Implement post-training optimization techniques (quantization, pruning, distillation) to improve efficiency and latency in real-time speech generation.
• Integrate and test novel architectures, such as neural codecs, diffusion, or flow-matching models, to enhance realism and responsiveness.
• Design and implement new evaluation metrics for TTS systems, including automated Mean Opinion Score (MOS) prediction models for continuous quality assessment.
• Stay updated with the latest research in audio diffusion, autoregressive models, neural codecs, and multimodal LLMs.

What we're looking for:

• Strong understanding of generative modelling, ideally applied to sequential or multimodal data.
• Hands-on experience with large language models (LLMs) or similar transformer-based architectures.
• High proficiency in PyTorch, including experience with distributed training and model optimization.
• Solid grasp of time-series modelling and tokenization, preferably in the context of audio or speech.
• Demonstrated ability to prototype quickly, test hypotheses, and iterate efficiently.
• Proven experience in training deep learning models end-to-end, from data preparation to evaluation.
• Strong general software engineering skills, enabling contributions to a large, shared research infrastructure.

Nice-to have experience

• Familiarity with state-of-the-art architectures in audio and speech generation (e.g., diffusion models, neural codecs, flow-matching models, autoregressive decoders).
• Experience with speech-to-speech or text-to-speech (TTS) systems.
• Evidence of original research contributions, such as publications or open-source work in top-tier venues (e.g., ICASSP, Interspeech, NeurIPS, ICML).

Why join us?

We’re living the golden age of AI. The next decade will yield the next iconic companies, and we dare to say we have what it takes to become one. Here’s why,

Our culture

At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here.

Serving 50,000+ customers (and 50% of the Fortune 500)

We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2.

Proprietary AI technology

Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind.

AI Safety, Ethics and Security

AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence's impact on our society is still unfolding, our position is clear: People first. Always. Learn more about our commitments to AI Ethics, Safety & Security.

The good stuff...

• Competitive compensation (salary + stock options + bonus)
• Fully remote from Europe or hybrid work setting with an office in London, Amsterdam, Zurich, Munich
• 25 days of annual leave + public holidays
• Great company culture with the option to join regular planning and socials at our hubs
• + other benefits depending on your location

You can see more about Who we are and How we work here: https://www.synthesia.io/careers

LI-MD1

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