Software Gigs

Sr. Director role overseeing SIE's data and AI platform ecosystem, including data infrastructure (streaming, ingestion, warehouse), experimentation platforms, and governance tools. Responsible for defining long-term vision, leading global engineering teams, and ensuring platforms process massive data volumes to deliver insights across gaming, content creation, and enterprise operations. Must drive scalability, cost efficiency, security, and operational excellence while collaborating with senior leadership to align platform strategy with business objectives. Requires proven Sr. Director-level experience and thought leadership in platform architecture, real-time data processing, and ML/AI integration.

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.

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.

Staff Software Engineer leading the design, architecture, and operations of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. The role involves architecting and evolving streaming and analytics platforms using Apache Flink, Spark, ClickHouse, and Druid; integrating batch and streaming workloads via lakehouse architectures; and embedding AI capabilities to automate engineering workflows and improve platform reliability. You will own critical platform capabilities end-to-end from design through production operations, mentor engineers, establish technical standards, and influence cross-functional technical strategy across PlayStation's data infrastructure.

Staff Software Engineer leading design and evolution of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. Responsibilities include architecting stream processing pipelines, optimizing data services using Apache Flink, Spark, ClickHouse, and Druid, integrating batch and streaming workloads with lakehouse architectures, and building AI-powered capabilities for platform automation. The role requires deep expertise in distributed systems, real-time analytics, and mentoring engineering teams while establishing technical standards and best practices across the organization.

The Data Architect is a hands-on technical leader responsible for designing and modernizing enterprise data architecture across on-premises and cloud platforms. The role defines scalable data models, integration patterns, and architectural standards for the Data Lake, Data Warehouse, ODS, and cloud platforms while ensuring security, resilience, and high performance. Working closely with engineering, analytics, and business teams, the architect provides technical guidance on modern data systems, governance frameworks, and ETL/ELT pipelines. The position combines strategic vision with practical experience optimizing structured, semi-structured, and unstructured workloads across SQL, NoSQL, and cloud-native systems.

Staff-level engineer designing and operating large-scale distributed data platforms powering real-time analytics across PlayStation. You will lead the architecture and evolution of the Real-Time Analytics Platform (RTAP), own critical capabilities end-to-end from design through production operations, and mentor engineers. Key responsibilities include building and optimizing distributed services using Apache Flink, Spark Structured Streaming, ClickHouse, and Apache Druid; integrating batch and streaming workloads with lakehouse architectures; and establishing technical standards for distributed systems, stream processing, and data modeling.

SAIC seeks a Senior Software Engineer to integrate AI capabilities into expeditionary systems for the Navy, including sensor kits, unmanned systems, command & control, and TAK. The role spans hands-on development—maintaining Zenoh data mesh in Rust, building React frontends in JavaScript, integrating machine learning models, converting tactical message formats—and technical leadership with senior management. Required qualifications include 9+ years as a software engineer, data engineer, or data scientist; expert Python; intermediate Rust or Go; experience with containerization, event streaming platforms (Kafka, Kinesis, RabbitMQ, Zenoh), tactical data standardization, and data pipeline deployment. Successful candidates will collaborate with ML engineers, systems engineers, and field teams to ensure high-quality data flow and operational performance.

The Data Architect is a hands-on technical leader responsible for designing and modernizing enterprise data architecture across on-premises and cloud platforms. The role defines scalable data models, cloud-native integration patterns, and architectural standards supporting analytics, reporting, and digital banking applications. This position combines strategic architectural vision with practical expertise in designing, implementing, and optimizing modern data systems across structured, semi-structured, and unstructured workloads. The architect provides technical leadership to Data Engineers and cross-functional teams while ensuring secure, resilient, and high-performing data environments.

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.

As Senior Manager of AI Governance Technology at Teradata, you will own the end-to-end technical architecture of the company's AI Governance Operating System, including intake pipelines, compliance monitoring, and agentic workflows. You'll lead the engineering of sophisticated automation using tools like n8n and Claude APIs, architect the AI tool registry, and design real-time monitoring systems that anticipate governance challenges. You'll serve as the internal expert on Claude Enterprise, Microsoft Copilot, and Teradata AI Studio integrations, setting technical standards and specifications that guide the broader engineering team. This is a technical leadership role requiring deep expertise in enterprise AI governance, system architecture, and the ability to make complex engineering decisions with significant autonomy.

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.

Design and develop software applications that orchestrate complex automated laboratory workflows and closed-loop learning systems for biotherapeutics discovery. Build data pipelines, visualization tools, and machine learning models for visual inspection, predictive maintenance, and process optimization. Create digital lab infrastructure including IoT integrations, digital lab twins, and telemetry monitoring. Requires a Ph.D. in Computer Science, Software Engineering, or Bioengineering with 3+ years of industry experience, or equivalent education with more experience; strong proficiency in Python, SQL, JavaScript, and DevOps practices; and familiarity with lab data platforms and autonomous systems.

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.

Join Axos Bank's Data Engineer Rotational Program as a first-career technologist gaining multi-platform exposure in data engineering. You will focus on SQL-based solutions, creating SSIS packages for data transformation, writing and maintaining SQL jobs and stored procedures, and performing data quality validation and testing. Through rotations across database development, data quality, and business intelligence reporting, you'll develop enterprise-level solutions while working with cross-functional teams on Salesforce, Marketing Cloud, and omnichannel platforms. The role requires a bachelor's degree in IT, Computer Science, or related field, with basic to intermediate SQL Server knowledge.

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.

Design, build, and scale ETL/ELT pipelines and cloud data warehouse infrastructure using Python, SQL, and modern orchestration tools like Apache Airflow or Prefect. Own the end-to-end data infrastructure that powers business intelligence and operational reporting, including data quality checks, anomaly detection, and performance optimization. Collaborate with analytics and software engineering teams to define data modeling standards and maintain infrastructure-as-code deployments using Terraform and CI/CD. Requires 3–6 years of data engineering or backend development experience with advanced SQL, Python, and hands-on work with cloud data warehouses like Snowflake or BigQuery.

Lead the strategy and delivery of enterprise discovery informatics platforms for pharmaceutical research, translating scientific needs into scalable technology capabilities and product roadmaps. Partner with business stakeholders to analyze chemistry and computational science workflows, define IT requirements, and modernize lead discovery and optimization processes across multi-site teams. Manage portfolio strategy, identify technology gaps, drive data-driven and AI-augmented discovery workflows, and ensure measurable business impact through effective project execution and risk management.

Senior Database Engineer II will build and maintain scalable data pipelines using Azure Data Factory, Databricks, Azure Synapse Analytics, and Snowflake. The role requires designing and implementing conceptual, logical, and physical data models, leading system integration efforts, and architecting multi-tenant database solutions for the medical device industry. You'll provide technical leadership on data engineering standards, conduct code and design reviews, and collaborate across geographically dispersed teams in an Agile environment. The position demands 7+ years of database design and software development experience, with a strong focus on healthcare compliance and data governance.

Design and develop advanced Power BI dashboards using DAX, Power Query, and complex data modeling techniques. Consolidate and transform data from APIs, files, and SQL databases into reliable reporting models using ETL workflows with SSIS and T-SQL. Build scalable reporting solutions and partner with business users to translate requirements into technical implementations in a fast-paced, multi-project environment. Requires 3+ years of hands-on experience with DAX, Power Query, T-SQL, SSIS, and strong SQL development skills including stored procedures and query optimization.

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.