Python AI Jobs

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Design and deploy AI solutions by working closely with customers to translate their challenges into functional agents, integrating APIs and data sources to automate real business processes. Prototype quickly, build the first version, get it into production, and refine based on real-world feedback. Collaborate with customer teams across engineering, product, and operations to ensure the agent performs, scales, and delivers measurable outcomes. Own end-to-end delivery from discovery call to deployment, leading the technical build, testing, and iteration to ensure the experience feels natural, human, and on-brand. Drive adoption and expansion by sharing results, training teams, and embedding within the customer organization to uncover new opportunities for automation and scale. Act as the face of Bland by being the customer’s champion, traveling on-site, developing real relationships with customers and stakeholders, hosting training sessions and dinners, and providing unreasonable hospitality.

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As Product Manager for Ghostwriter, you will define how humans interact with software in the agent era, owning the end-to-end product experience from prompt to agent to outcome and helping scale Sierra to thousands of customers. You will define how AI augments agent development by shaping the workflows by which CX teams and developers draft journeys, run simulations, analyze conversations, and improve agents using natural language. You will balance autonomy and control by designing the appropriate human-in-the-loop patterns such as approval flows, change review, and workspace isolation to ensure customer trust in Ghostwriter's changes to their agents. You will partner closely with AI/ML and platform engineering teams to collaborate on model selection, harness engineering, execution architecture, and evaluation/testing infrastructure. Additionally, you will act as the voice of the agent builder by deeply understanding the pain points of CX managers, agent developers, and technical teams configuring journeys, integrations, and simulations.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time speech pipeline systems including live audio buffering, streaming, latency optimization, and integrating with speech providers. You will build and improve conversation intelligence systems that manage the LLM layer for natural conversation flow, including prompt construction, context management, function calling, and dialogue management. You will develop the action framework that allows the AI Voice Agent to execute tasks during calls such as querying account data, creating tickets, and checking order status, handling API configuration, success/failure branching, authentication management, and runtime execution. Additionally, you will work on knowledge ingestion, storage, and retrieval for the voice agent and manage memory for retaining information across conversations to improve responses. You will collaborate with designers to create easy-to-use interfaces for agent lifecycle management including creation, configuration, testing, and deployment. You will contribute to building evaluation frameworks and metrics for voice AI quality, post-call analytics, and instrumentation, as well as participate in the on-call rotation.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio such as buffering, streaming, and latency optimization, along with integrating speech providers. You will build and improve conversation intelligence systems, including prompt construction, context management, function calling, and dialogue management to make conversations feel natural. You will develop the action framework to execute configurable API calls, manage success/failure branching, authentication, and runtime execution during calls. You will work on knowledge ingestion, storage, retrieval, memory, and context for the voice agent to improve its performance over time. Additionally, you will collaborate on agent lifecycle tasks such as creation, configuration, testing, and deployment of voice agents and help build evaluation frameworks for model performance, call quality metrics, and call analytics. Participation in on-call rotations is also expected.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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As a Model Optimization & Deployment Engineer, you will optimize large-scale models (LLMs, VLMs) using advanced quantization techniques such as PTQ, QAT, mixed-precision inference workflows, and parameter-efficient fine-tuning methods like LoRA and QLoRA. You will architect and implement model conversion and compilation pipelines using TensorRT and TensorRT-LLM for deployment on edge devices. The role involves performing rigorous parity checking, accuracy recovery, and latency benchmarking between PyTorch frameworks and compiled edge binaries. You are responsible for writing and optimizing custom CUDA kernels and TensorRT Plugins to maximize memory bandwidth and minimize latency on AI accelerators. Furthermore, you will write production-level, highly concurrent, memory-safe C++ and Python code to ensure real-time, deterministic execution of inference on vehicle System on Chips (SOCs).

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Building the best-in-the-world objects manipulation system with a cross-functional R&D team including Hardware, Software, and AI; inventing and implementing strategies for using new robotic grippers to handle previously unpickable items; improving adaptability, scalability, and reliability of the robotic platform; using data to build heuristics for handling different categories of items; and detecting anomalies using a combination of signals to determine if a robot picked more than one item at once or if one item is disassembling.

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The Deployment Lead will work closely with Simulation Engineers, Machine Learning Engineers, and customers to understand and define engineering and physics challenges, providing technical leadership to their team. Responsibilities include leading pre-processing and analysis of complex data for predictive modelling, establishing best practices, architecting and developing innovative deep learning models combined with optimisation methods, taking responsibility for the quality and impact of their work and their team's work, designing and testing robust, scalable data pipelines for production environments, leading cross-functional collaboration for seamless integration of data science models with simulations, driving internal R&D and product development to refine models and identify new applications, mentoring junior team members, leading communication and presentations to technical teams and customers, onboarding users and co-developing solutions with customers. The role involves representing the company as a technical authority at customer sites internationally, collaborating on-site to build solutions, influencing technical direction, shaping future solutions and products, and developing leadership skills.

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As a Senior Modeling & Simulation Engineer, responsibilities include developing models and infrastructure for the integrated simulation pipeline in C++, designing deterministic, high-performance simulation tools capable of faster-than-real-time execution for development, testing, and release, implementing test scenarios and writing unit, system, and regression tests. Collaborate across autonomy, embedded, GNC, and test engineering teams to ensure the simulation mirrors real aircraft behavior and mission scenarios. Contribute to platform-agnostic simulation tooling to accelerate future development efforts. Perform verification and validation (V&V) analysis on model tools. Conduct system performance analysis and generate reports and visualizations. Utilize best practices in C++, simulation architecture, and performance engineering.

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As a Staff Software Engineer in the Foundations department, responsibilities include leading the design and implementation of highly scalable systems for the Managed AI offerings, driving the long-term technical roadmap for the Foundations team to support growth and evolving AI workloads, working cross-functionally with Cloud Engineering to align technical goals and solve integration challenges, leading by example through high-quality code contributions and mentoring Senior and Staff-level engineers, championing reliability, observability, and performance by identifying and resolving systemic bottlenecks, and staying current with AI infrastructure trends to ensure efficient and powerful tools are utilized.