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.

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.

Senior Python Backend Engineer building and maintaining Python-based APIs and services in an AWS environment. The role focuses on developing Kafka-based data pipelines, managing data movement across systems, and ensuring reliability and performance of containerized services. You'll need strong production Python experience, familiarity with streaming or message-based systems, and comfort working with AWS and Docker.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams. The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team. The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Lead day-to-day technical project delivery for Distribution Operations initiatives including outage management, solar/battery data integrations, forecasting, automation, and analytics. Transform technical requirements into delivery plans, sprint backlogs, and release milestones while coordinating work across data engineers, architects, cybersecurity teams, and vendors. Manage project controls, scheduling, defect tracking, and comprehensive documentation across integrations, ETL/ELT pipelines, cloud analytics, and Power Platform solutions. Facilitate design reviews, support operational readiness, and ensure technical solutions meet engineering rigor and real-world operational needs.

Lead the architectural design of server-side, backend, and cloud platform capabilities supporting Dexcom's continuous glucose monitoring products. Collaborate across product, cybersecurity, firmware, data, and mobile teams to translate business and regulatory requirements into secure, scalable, and maintainable platform solutions. Establish architectural direction for distributed services, APIs, data pipelines, event-driven systems, and shared platform capabilities. Guide technology exploration, system integration, performance testing, and serve as an authority for platform architecture principles and modern backend development practices across the organization.

As a client-facing data analyst, you'll work with Department of the Navy clients to understand their data needs, transform complex structured and unstructured data into actionable insights, and advise on how findings impact DoD acquisition programs and IT modernization. You'll use tools like Tableau, Python, R, or SQL for data manipulation; Databricks or Spark for data engineering; and Power Apps/Power Automate for automation. You must hold or be able to obtain a Secret clearance and have 4+ years of DoD client experience.

Lead the entire engineering team at a VC-backed AI trading firm, combining hands-on ML research and model development with technical leadership. Own the end-to-end development of production AI systems deployed in live financial markets, including model design, training, evaluation, and infrastructure. Set technical direction across ML and software engineers, drive hiring and team development, and translate research into measurable real-world trading performance. Require 5–8 years of applied ML/AI experience with 3+ years managing full engineering teams.

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.