Full-Time
Software Test Engineer
Posted on 8/21/2026
Bigbear.ai
501-1,000 employees
Operational AI platform for decision support
No salary listed
Remote in USA
Remote
Applicants must currently reside in the United States.
 QA & Testing (1) |
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- 5-8 years of experience with software testing.
- Strong software engineering background with deep experience in automated test development.
- Experience testing backend services, application programming interfaces, distributed systems, and database-backed applications.
- Experience designing smoke, unit, integration, and end-to-end testing strategies.
- Strong judgment around edge cases, non-happy paths, adversarial inputs, regression risk, and failure isolation.
- Experience improving continuous integration test reliability, execution time, parallelization, test isolation, and failure observability.
- Familiarity with testing Defense in Depth behavior by validating that multiple layers of checks work together.
- Strong troubleshooting, analytical, and communication skills.
- Ability to work independently and as part of a team.
- Ability to obtain clearance.
- Develop and maintain smoke tests, unit tests, integration tests, and end-to-end tests across backend and platform workflows.
- Validate backend changes for new pull requests through automated test coverage, direct API testing, and user-facing workflow verification where appropriate.
- Exercise backend behavior through the user interface when useful, without owning visual design, frontend user-experience validation, or manual user-interface regression testing.
- Build regression coverage for edge cases, malformed inputs, authorization boundaries, concurrency issues, failure modes, and other non-happy-path scenarios.
- Improve the scalability, determinism, execution performance, maintainability, and diagnostic quality of the test suite.
- Strengthen test fixtures, mocks, test data management, failure analysis, and continuous-integration feedback loops.
- Design end-to-end tests for AI platform workflows while minimizing unnecessary token usage, external provider calls, latency, and test cost.
- Apply AI-assisted development and analysis tools to accelerate test design, test generation, triage, and maintenance while preserving reliability, reviewability, performance, and deterministic validation standards.
- Validate backend APIs, authentication flows, billing and token behavior, model routing, AI workflow execution, file parsing, MCP/tool execution, and passthrough APIs.
- Validate AI platform end-to-end paths involving prompts, model responses, streaming behavior, tool calls, agents, workflow orchestration, and provider-facing API compatibility.
- Validate agentic harnesses, MCP integrations, and workflow automation systems, including concepts common to no-code and low-code workflow builders.
- Validate security-sensitive product behavior such as user isolation, permission boundaries, validation, sanitization, rate limits, replay prevention, safe error handling, and layered control behavior.
- Build and maintain test infrastructure that scales with a growing platform, expanding product surface area, and active engineering team.
- Experience writing end-to-end tests for AI platforms, large language model applications, model gateways, agents, MCP tools, or tool-using systems.
- Experience designing AI end-to-end tests that control token usage, external provider calls, latency, and cost.
- Experience testing workflow automation systems, agentic harnesses, or no-code/low-code automation platforms such as Power Automate, Zapier, Make, and n8n.
- Familiarity with generative AI provider APIs such as Google Vertex AI, Amazon Web Services Bedrock, and Microsoft Azure OpenAI, and models such as OpenAI GPT, Anthropic Claude, and Google Gemini.
- Experience with continuous integration and continuous delivery pipelines such as GitHub Actions, Docker, Kubernetes, and observability or monitoring tooling.
- Experience with PostgreSQL and SQL for validating stored data.
- Knowledge of government compliance frameworks including FedRAMP, NIST AI RMF, and CMMC 2.0.
BigBear.ai builds operational AI software that helps organizations improve decision-making, cyber operations, and business agility. Its platform combines AI-driven analytics on an organization’s data with external inputs to give a full, real-time view of the environment, uncover new insights, and produce accurate predictions. The product works by integrating data from internal systems and external sources, applying advanced analytics to surface actionable intelligence, and enabling fast, informed actions in complex settings. What sets BigBear.ai apart is its depth of expertise in operational AI and its ability to optimize existing data with additional inputs, delivering data mining and predictive capabilities that go beyond traditional intelligence tools. Its goal is to help clients succeed in mission-critical environments—such as government intelligence, healthcare, and manufacturing—by improving efficiency, risk management, and operational performance.
Company Size
501-1,000
Company Stage
IPO
Headquarters
Columbia, Maryland
Founded
2020
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Simplify's Take
What believers are saying
- Q2 2026 revenue rose 13% to $36.7 million, with margins expanding.
- Management announced 20+ new contracts and $269.6 million backlog on July 30, 2026.
- BigBear.ai affirmed 2026 revenue guidance of $135 million-$165 million despite volatility.
What critics are saying
- July 31, 2026 ATM permits 100 million shares, creating heavy dilution risk.
- Q2 2026 still burned $22.2 million operating cash and lost $25.7 million.
- Government contract concentration makes one Pentagon procurement cut a near-term existential threat.
What makes Bigbear.ai unique
- BigBear.ai sells air-gapped GenAI for Department of War missions, announced August 2026.
- Sean Gainey joined the board August 13, 2026, deepening defense-operations credibility.
- Pangiam Threat Detection won Dutch approval for airport screening on August 2026.
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↑ 2%BigBear.ai appoints retired U.S. Army Lt. Gen. Sean A. Gainey to Board of Directors. Foto: lightpoet - stock.adobe.com BigBear.ai (NYSE: BBAI), a specialized defense and security technology company providing mission-ready AI, today announced the appointment of retired U.S. Army Lt. Gen. Sean A. Gainey to its Board of Directors, effective August 13, 2026. Gainey joins the Board after a 36-year Army career in which he helped define the Joint Force's modern counter-drone approach and later commanded JTF-Gold, Golden Dome's operational arm. Retired U.S. Army Lt. Gen. Sean A. Gainey "General Gainey's experience, expertise and operational insights make him a very valuable addition to the BigBear.ai Board," said Peter Cannito, Chair of the Board of Directors of BigBear.ai. "Few leaders have his firsthand understanding of how a complex defense architecture becomes a fielded, trusted capability. His perspective will strengthen our Board and the direction we set for BigBear.ai." Gainey became the first director of the department's Joint C-sUAS Office in 2020, when the services had roughly 40 separate C-sUAS systems without a common joint architecture. Over the next four years, he led the office as it delivered the department's first C-sUAS strategy and advanced the operational concepts, joint doctrine, training standards, and open command-and-control architecture needed to connect sensors and defensive systems across services. During his tenure, the Joint C-sUAS University opened at Fort Sill to establish a common training foundation for operators across the force. In his final Army assignment, Gainey served as commanding general of U.S. Army Space and Missile Defense Command and commander of the Joint Functional Component Command for Integrated Missile Defense. In January 2026, he also assumed command of JTF-Gold. U.S. Northern Command established the task force to integrate and synchronize the homeland's air and missile defense enterprise and serve as the operational arm for future layered defense systems provided by Golden Dome. "General Gainey has spent his career at the leading edge of some of the most urgent threats facing warfighters, from counter-drone and integrated missile defense to joint force integration across combatant commands at the center of national security missions," said Kevin McAleenan, Chief Executive Officer of BigBear.ai. "As the threat from unmanned systems and advanced missile technology continues to accelerate, his firsthand operational understanding of what our men and women in uniform need from AI-enabled decision support is exactly the perspective our Board and our company need. We are honored to welcome him." Diskutieren sie über die enthaltenen Werte. Business Wire (engl.) Autor folgen Verfasst von Letzte Änderung17.08.2026, 15:15 Im Artikel enthaltene Werte
BigBear.ai has appointed retired US Army Lt Gen Sean Gainey to its board of directors, effective 13 August 2026. Gainey joins after a 36-year military career, including serving as the first director of the Department of Defense's Joint Counter-Unmanned Aircraft Systems Office from 2020. In that role, he helped develop the department's first counter-drone strategy and established common training standards across military services. In his final assignment, Gainey commanded US Army Space and Missile Defense Command and led JTF-Gold, the operational arm of the Golden Dome homeland defence system. The McLean, Virginia-based defence technology company develops AI-powered solutions for national security applications. Chief executive Kevin McAleenan said Gainey's operational experience with AI-enabled decision support systems aligns with the company's mission to support military personnel.
BigBear.ai and Texas Instruments represent contrasting investment approaches in the technology sector. BigBear.ai sells AI-driven decision support tools to the US Intelligence Community and Department of Defense. The company recently acquired Pangiam to expand its biometrics capabilities. In FY 2025, BigBear.ai's revenue fell 19.3% to $127.7 million. The company reported a net loss of $293.9 million, producing a negative net margin of 230.2%. Free cash flow was negative $46.3 million. Its debt-to-equity ratio stood at 0.19x. Texas Instruments produces analog and embedded processors for over 100,000 customers. Industrial and automotive markets represent 66% of total revenue. In FY 2025, Texas Instruments' revenue increased 13% to $17.7 billion. Net income reached $5.0 billion, with a net margin of 28.3%.
BigBear.ai Holdings reported winning over 20 new contracts in the past quarter, alongside a 9% increase in backlog, as the company focuses on applied AI for defence and security. The firm is expanding its secure, air-gapped generative AI platform for US Department of Defence customers, connecting autonomous defence systems in sensitive environments through its ConductorOS platform. The company's narrative projects $195.5 million revenue and $15.3 million earnings by 2029, requiring 14.1% yearly revenue growth. However, investors face near-term risks from lumpy government spending and continued cash burn. The contract wins aim to build a more predictable revenue base, though BigBear.ai continues to operate at a loss. Some analysts estimate revenue could reach $188.1 million by 2029, with fair value estimates around $4.00 per share.
BigBear.ai and IonQ represent contrasting investment opportunities in the tech sector. BigBear.ai provides AI-driven decision intelligence solutions primarily to US federal agencies, focusing on logistics and cybersecurity challenges. In fiscal 2025, BigBear.ai reported revenue of $127.7 million, down 19.3% year-over-year, with a net loss of $293.9 million. The company maintains a current ratio of 1.8x and zero debt-to-equity ratio, though free cash flow was negative $42.5 million. IonQ develops trapped-ion quantum computing systems for applications in medicine, finance, and logistics, delivering services through cloud platforms including Amazon's. The company acquired SkyWater Technology in July 2026 to establish its own semiconductor foundry. Both companies face distinct challenges: BigBear.ai's heavy reliance on government contracts creates concentration risk, whilst IonQ pursues longer-term quantum computing opportunities.