Miami Shores, FL, United States Job Description Role Overview: This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models. For example, for SFT data generation, you might have to put together or be provided a prompt which contains provided code and questions, you will then provide the model responses, and write corresponding Python code to solve the questions. For RLHF data generation, you may need to create a prompt yourself or use one provided by the customer, ask the model questions, and evaluate the outputs generated by two versions of the LLM. You'll compare these outputs and provide feedback, which is then used to fine-tune the models. Please note that this role does not involve building or fine-tuning LLMs. What does day-to-day look like: Design, develop, and maintain efficient, high-quality Python code to train and optimize AI models. Conduct evaluations (Evals) to benchmark model performance and analyze results for continuous improvement. Familiarity with Python frameworks and libraries Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria. Develop comprehensive explanations and rationales for evaluations, showcasing excellent reasoning and technical expertise. Lead efforts in Supervised Fine-Tuning (SFT), including creating and maintaining high-quality, task-specific datasets. Collaborate with researchers and annotators to execute Reinforcement Learning with Human * Feedback (RLHF) and refine reward models. Design innovative evaluation strategies and processes to improve the model's alignment with user needs and ethical guidelines. Create and refine optimal responses to improve AI performance, emphasizing clarity, relevance, and technical accuracy. Conduct thorough peer reviews of code and documentation, providing constructive feedback and identifying areas for improvement. Collaborate with cross-functional teams to improve model performance and contribute to product enhancements. Continuously explore and integrate new tools, techniques, and methodologies to enhance AI training processes. Requirements: 3+ years of strong experience with Python programming language. Industry experience and knowledge of code quality, formatting, and best practices of software development Experience with Python’s testing ecosystem, including unit, integration, and property-based testing. Knowledge of multi-threading and asynchronous programming in Python. Ability to work with architectural patterns and refactor code without introducing regressions. Strong debugging skills, including fixing memory and concurrency issues. Fluent in conversational and written English communication skills

Anywhere, Anywhere, USA The Role You'd be joining a lean, high-leverage engineering team tackling the complexity of legal software — not by pushing longer hours, but by getting genuinely good at working alongside AI. This isn't a "we occasionally use ChatGPT" team. The group is actively figuring out how to hand repetitive engineering work to AI so people can spend their time on the problems that actually need human judgment. If you like tight feedback loops and think of AI as a real engineering collaborator rather than a novelty, this is that kind of team. Compensation, Benefits & Culture Founded by two repeat entrepreneurs who set out to build a company without the usual startup burnout culture — their previous venture picked up multiple workplace-quality awards. The team protects: A sustainable pace Long stretches of uninterrupted deep work A light meeting calendar Comp: USD $135,000 - $185,000 DOE, equity grant appropriate to the role, standard ~40-hour week. Benefits: Fully remote — work from anywhere Medical, dental, and vision for you and your family Annual stipends toward learning/development and personal wellness Retirement plan with an employer contribution regardless of what you personally contribute Flexible PTO (vacation, sick time, holidays) Paid parental leave for either parent What You'll Do Help push the engineering org toward an AI-native way of building software Keep improving internal dev workflows — using AI to ship faster, automate regression coverage, and hold the line on code quality Design, build, ship, and support full-stack systems end to end Work cross-functionally to deliver complete solutions spanning AI, modern web tooling, data stores, and backend platforms Write code that's scalable, reusable, and easy for the next person to maintain Make sure what you ship works well across browser and mobile Build automated test coverage into new work as you go, not after Take part in the on-call rotation Partner with architects and peers on system design, dependency choices, and long-term maintainability Split time between new feature work and keeping existing systems healthy Communicate clearly with non-engineering stakeholders Mentor more junior engineers and help set technical direction What We're Looking For 7+ years of professional, commercial software development — shipping, deploying, maintaining, and debugging real production systems — plus: A track record of actually improving how a team ships software using modern AI tooling (not just personal productivity hacks) Hands-on experience with AI coding agents (e.g., tools like Claude Code, Codex, or similar) as part of your regular workflow Experience pointing AI tooling at large-scale refactors, modularization, and code analysis Solid Python / Django experience Solid React experience GraphQL experience (Apollo Client and/or Relay) Comfort writing real unit test coverage Nice to Have AWS, Docker, Terraform MongoDB, PostgreSQL Git Experience with third-party integrations — things like O365, DocuSign, generic REST APIs, Salesforce, SAML 2.0, or Zapier Exposure to monitoring/alerting tooling Who Does Well Here Sharp analytical and problem-solving instincts Real curiosity about LLMs and AI-assisted engineering — not just tolerance for it Comfortable with shifting priorities and some ambiguity Cares about the end-user experience, not just the code Self-directed, high agency, high accountability Takes pride in the craft and welcomes feedback Communicates and documents clearly Genuinely curious, always picking up new tools Operates with integrity and sound judgment

Hyderabad, Telangana, India We are hiring a Python Backend Engineer to take ownership of high-throughput backend services. You will work alongside our front-end and C# application teams to build the infrastructure that ingests, processes, and routes real-time data streams across our platforms. This role is for someone who enjoys asynchronous Python, complex data-flow problems, and the discipline of building production systems that need to run reliably under sustained load. Experience: 4+ years of professional backend software engineering experience, primarily in Python. Asynchronous Python: Strong working knowledge of asyncio and concurrent programming patterns for high-throughput I/O. API Frameworks: Production experience with FastAPI (or comparable async frameworks such as Starlette, aiohttp, or Sanic) and pydantic-based schema validation. Message Brokers: Hands-on experience with at least one event streaming or message broker platform — Apache Kafka, RabbitMQ, NATS, or equivalent. Databases: Proficient with PostgreSQL and capable of writing optimized SQL; comfortable with schema design, indexing, and query profiling. Time-Series Data: Working familiarity with time-series databases (e.g., TimescaleDB, InfluxDB, QuestDB) and the trade-offs of heavy-write workloads. Networking: Solid grasp of TCP/UDP, WebSockets, and HTTP/REST; able to debug at the protocol level when needed. Platform & Tooling: Comfortable Git-based workflows. Testing & Quality: Demonstrated practice with pytest, unit and integration testing, and CI pipelines.