Software Gigs

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs. Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

This is a hands-on security engineering role focused on running Saronic's cyber threat intelligence program. The engineer will track nation-state and advanced criminal adversaries targeting the defense industrial base, operationalize indicators and TTPs into detections and hunts, produce finished intelligence products, and fuse external threat feeds with internal telemetry to prioritize real exposures. The role requires 4+ years in cyber threat intelligence, threat hunting, detection engineering, or intrusion analysis with demonstrable experience tracking sophisticated or state-sponsored adversaries.

As a Senior Systems Engineer for Data Center AI at Qualcomm, you will research, develop, optimize, and validate AI/ML solutions that integrate Qualcomm's hardware accelerators with software and ecosystem to deliver high-performance inference in datacenter environments. You will design and deploy Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking, and drive system-level architecture decisions. You will collaborate across functional teams to meet system-level requirements while staying current with advances in AI/ML models and hardware. Required skills include strong Python proficiency, expertise in ML frameworks (PyTorch, TensorFlow), deep understanding of ML deployment and system performance profiling, and experience with parallel computing.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace. Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers. Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and internal workflows. The role involves implementing end-to-end AI pipelines using AWS services (SageMaker, Lambda, ECS/EKS, S3), building feature engineering workflows in Snowflake, and collaborating with cloud and MLOps teams. Candidates need 3+ years of AI/ML engineering experience with 1–2 years focused on generative AI or LLMs, proficiency in Python and frameworks like PyTorch or Hugging Face Transformers, and hands-on cloud deployment experience. The position requires monitoring model performance, ensuring security and compliance, and supporting AI architecture reviews.

Qualcomm seeks a Machine Learning R&D leader to design and deliver Agentic AI systems for hardware design automation. The role combines hands-on technical execution with team leadership, requiring deep expertise in generative AI, large language models, and ML/AI product development. Responsibilities include architecting end-to-end ML solutions, coaching an interdisciplinary team of researchers and engineers, and bridging research prototypes into production design tools. The ideal candidate brings PhD-level credentials, 2+ years of industry ML/GenAI experience, and proven expertise in agentic AI frameworks, foundation models, and complex ML software pipelines.

Lead Veracyte's Quality Engineering Platform team as Manager of Software Test Automation, defining quality strategy and building scalable automation frameworks that enable product teams to own software quality. Manage a team of SDETs, Automation Engineers, and Quality Platform Engineers to develop self-service testing platforms, reusable automation libraries, and AI-assisted quality tools supporting unit, integration, API, UI, end-to-end, regression, performance, security, and compliance testing. Establish modern engineering practices, quality standards, and reference architectures across the organization while fostering a culture of innovation and continuous improvement. Drive Veracyte's strategy for AI throughout the software testing lifecycle, including automated test generation, regression analysis, defect classification, and synthetic test data creation.

This intermediate-level role develops AI-enhanced decision systems for government sponsors, applying machine learning, large language models, and decision science to mission-critical problems. You will design, prototype, and evaluate AI solutions in cross-functional teams, lead R&D initiatives, and work hands-on with modern AI techniques including deep learning, reinforcement learning, generative AI, and multi-agent systems. The role requires 2+ years of hands-on ML experience (or 1 year with a Master's degree) and proficiency in Python and/or C++, with the ability to communicate complex technical concepts to both technical and non-technical audiences.

Junior AI Software Developer supporting Navy cybersecurity and mission operations in San Diego. You will participate in problem discovery with mission stakeholders, contribute to technical solution design, and deliver hands-on implementation of scripts, APIs, automation, and AI/LLM-enabled workflows. Work involves integrating systems via REST/HTTP APIs, writing and testing Python code, using version control, and documenting technical solutions for both technical and non-technical audiences while adhering to RMF, security controls, and responsible AI practices in classified or controlled environments.

Senior AI Software Developer who will design, implement, and integrate AI/LLM-enabled solutions and automation for Navy cybersecurity and mission workflows. You'll work in small, multidisciplinary teams translating mission requirements into technical approaches, delivering hands-on code (Python, APIs, data integration), and ensuring solutions align with RMF, security controls, and compliance boundaries. The role blends advisory conversations with focused implementation of secure, deployable systems that reduce cyber risk and improve operational efficiency. Success requires 4+ years of software development or integration experience, Python proficiency, API and data store expertise, cloud or enterprise deployment knowledge, and ability to communicate across technical and non-technical stakeholders.

An AI Software Developer at Booz Allen Hamilton will design, implement, and integrate AI-enabled automation and analytics solutions for Navy cybersecurity and mission workflows. The role requires hands-on Python development, API integration, and secure system design in classified or controlled environments, with close collaboration between cybersecurity specialists, engineers, and mission stakeholders. Success demands translating mission requirements into technical tasks, delivering credible implementations (not slide-only recommendations), and operating within RMF, compliance, and secure engineering practices. The ideal candidate combines software engineering rigor (version control, code review, testing) with AI/LLM experience and the ability to work in small, structured teams on multidisciplinary problem-solving.

As a Sr. Staff Software Engineer on Qualcomm's AI Software team, you will design and develop the Qualcomm AI Runtime (QAIRT) SDK for IoT platforms, enabling efficient execution of deep neural networks, LLMs, and modern AI architectures. You will lead feature development, mentor junior engineers, and collaborate across cross-functional teams to deliver scalable AI software solutions. The role requires 8+ years of software development experience with strong C/C++ and Linux proficiency, hands-on technical leadership, and the ability to keep pace with rapid industry and academic advances in machine learning and software engineering.

Design and implement software integrations connecting lab automation infrastructure (liquid handlers, plate readers, robotic workstations, analytical instruments) with orchestration layers and digital workflows. Build production-grade Python applications and containerized services using Docker and Kubernetes, deploy CI/CD pipelines, and maintain LIMS integrations. Explore AI-driven decision-making and autonomous orchestration capabilities, staying current with LLM tooling and agent frameworks. Provide technical leadership and mentorship to the automation engineering team while shaping the roadmap for closed-loop autonomous discovery systems.

As an AI Software Engineer at Qualcomm, you will develop and optimize machine learning software for the Qualcomm AI Stack (QAIRT and Genie SDKs), enabling efficient execution of large language models and deep neural networks on Snapdragon platforms. You will validate and optimize performance of generative AI models, debug complex issues, and collaborate across teams to deliver robust AI solutions. The role requires strong software design, programming, and debugging skills, with expertise in machine learning frameworks, embedded systems optimization, and languages like Python or C++. You will work with minimal supervision, participate in design reviews, and contribute to a culture of technical excellence.

The Implementation Manager leads customer implementations of the Moveworks Agentic AI platform, overseeing on-time, on-budget delivery and ensuring projects follow leading practice methodology. Responsibilities include managing engagement governance, timeline, scope, risk, and resourcing; helping customers build and launch agentic workflows; and providing change management guidance for adoption and organizational readiness. The role requires collaborating with customer teams, partners, and internal stakeholders (Sales, services) to understand customer goals, identify gaps between plan and actuals, and serve as the primary point of contact for project success. Success demands mentoring team members and partners, tracking progress, and managing deviations through appropriate mitigation strategies.

Senior Applied AI Engineer at West Health will design, build, and deploy production-quality AI-powered tools and workflows using large language models, multi-agent architectures, and orchestration platforms. The role involves building intelligent automation pipelines with tools like n8n, LangChain/LangGraph, and Microsoft Copilot Studio, integrating AI capabilities with existing data infrastructure (Snowflake, Python), and conducting data analysis to identify high-impact AI opportunities. The ideal candidate combines deep practical expertise in applied AI with the ability to rapidly move from concept to fully working, well-documented applications that create measurable value for healthcare delivery and senior care initiatives.

Lead end-to-end development of AI agents and autonomous engineering tools for Teradata's enterprise platform, managing multiple global engineering teams. Shape engineering strategy for agent platforms, LLM gateways, vector stores, observability frameworks, and low-code/no-code/pro-code agent frameworks. Drive architecture standardization, governance practices, operationalization pipelines, and ROI metrics while partnering with product, architecture, and AI platform teams. Requires 12+ years software engineering experience with 5+ years in technical leadership, hands-on expertise with LLMs, agent frameworks, distributed systems, cloud platforms (AWS, Azure, GCP), and modern CI/CD tools.

Design, develop, and deploy end-to-end generative AI solutions including GenAI agents, LLMs, and applications to solve complex business challenges. Build data pipelines, design APIs for AI accessibility, and collaborate with cross-functional teams (developers, data scientists, business) to implement enterprise GenAI solutions. Requires 4+ years of full-stack application development (Java, Python, JavaScript) and 3+ years with data structures, algorithms, and data stores. Stay current with AI research and communicate technical concepts to non-technical stakeholders.

Staff Software Developer who will design, develop, and maintain AI-enabled knowledge management and decision-support platforms for environmental and engineering applications. Responsibilities include building data ingestion workflows, implementing retrieval-augmented generation (RAG) systems with vector embeddings and semantic search, developing backend services and APIs, creating secure web-based UIs, and working with cloud services (Azure AI Search, Blob Storage, PostgreSQL). The role requires strong software development skills demonstrated through a public code portfolio, familiarity with LLMs and prompt engineering, and the ability to collaborate across technical teams to translate project needs into practical features.