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At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 220 employees , we operate with startup agility and ambition. Our vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security. The Role Mach Industries is building autonomous systems that must be thoroughly tested and validated in a multi layered process, long before they are flown. As a Senior Software Engineer, Infrastructure & Simulation, you will build the simulation and validation infrastructure that enables rapid iteration and high confidence in real-world performance. Your work will sit at the foundation of our autonomy stack, powering Monte Carlo, software-in-the-loop (SITL), hardware-in-the-loop (HITL) and large-scale simulations across Mach product lines. Key Responsibilities - Design, build, and maintain simulation and validation infrastructure for aircraft systems, including discrete simulations of avionics, sensors, power distribution, flight controls, and embedded software. - Develop and operate Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) frameworks to support rapid development, regression testing, and system validation across flight autonomy and embedded stacks. -", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "seniority": "senior", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "employer_industry": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.10fa8faa33de8c08f0", "query_id": "gq_f4f646ef823e5abb2904", "job_id": "4e6983589ca4b6515cc1bc664a01ba8a", "signal_type": "query_match", "display_text": "Description: \"loop\"", "tooltip": "Description matched \"loop\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Software Engineer, Infrastructure & Simulation Huntington Beach, CA, Huntington Beach, California, United States About Mach Industries Founded in 2022, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms . 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Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company's solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co . We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. 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You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities. 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This role sits at the intersection of platform infrastructure and agent systems engineering. You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production. You'll work on systems that directly impact marketing, sales, service, and product growth verticals across the organization. This isn't a traditional infrastructure role. You should be comfortable wearing multiple hats of software engineering, agent systems design, and evaluation tooling. We're looking for engineers who think in flywheels: build →evaluate → improve → ship → repeat. What You'll Do Agent Harness & Flywheel Engineering Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops. 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This role sits at the intersection of platform infrastructure and agent systems engineering. You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production. You'll work on systems that directly impact marketing, sales, service, and product growth verticals across the organization. This isn't a traditional infrastructure role. You should be comfortable wearing multiple hats of software engineering, agent systems design, and evaluation tooling. We're looking for engineers who think in flywheels: build →evaluate → improve → ship → repeat. 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Implement agentic loop patterns with multi-turn reasoning, tool orchestration, memory management, and structured output handling as reusable platform primitives Build the agent flywheel: automated pipelines that collect agent traces, surface regressions, route failures to evaluation, and close the loop from production signal back to prompt/model", "matched_input": "loop", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_f4f646ef823e5abb2904:4db91df6cd221d1f8be2622e6b8406d3:description:loop", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.9f8924ba1828b9536f", "query_id": "gq_f4f646ef823e5abb2904", "job_id": "4db91df6cd221d1f8be2622e6b8406d3", "signal_type": "query_match", "display_text": "Description: \"loop\"", "tooltip": "Description contains \"loop\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Principal AI Engineer California - San Francisco; Washington - Seattle; Illinois - Chicago; California - Palo Alto; New York - New York; Washington - Bellevue We are seeking a highly skilled AI Platform Engineer to play a pivotal role in building the next generation of our ML/AI platform that doesn't just support ML models, but powers autonomous AI agents at enterprise scale. This role sits at the intersection of platform infrastructure and agent systems engineering. You'll build and maintain the core infrastructure, CI/CD pipelines, and platform services that underpin our machine learning initiatives and go further in designing the harnesses, sandboxes, and evaluation frameworks that let AI agents be developed, tested, and trusted in production. You'll work on systems that directly impact marketing, sales, service, and product growth verticals across the organization. This isn't a traditional infrastructure role. You should be comfortable wearing multiple hats of software engineering, agent systems design, and evaluation tooling. We're looking for engineers who think in flywheels: build →evaluate → improve → ship → repeat. What You'll Do Agent Harness & Flywheel Engineering Design and build agent harness infrastructure: the scaffolding that wraps LLM calls, manages tool use, handles retries, enforces policy, and feeds results back into iterative improvement loops. 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Within Life Science AI (LSAI), Lila develops large-scale generative models and reasoning frameworks - spanning biological sequences, molecular structures, and multimodal experimental data - that power automated scientific discovery and feed Lila's closed-loop \"Lab-in-the-Loop\" discovery engine. As that work scales, the surface area of cross-functional coordination across model research, evaluation, experimental science, and downstream integration is growing fast. We are looking for a Senior or Principal Technical Program Manager to join our AI Research team and bring structure, accountability, and delivery cadence to Life Sciences AI. You will be the connective tissue between model researchers, computational and wet-lab scientists, automation and software teams, and the broader Lila organization. The successful candidate thrives in ambiguity, communicates exceptionally well across audiences, and knows how to build clarity and momentum on a fast-paced, rapidly scaling team. 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What You'll Be Building - Establish and operate the delivery cadence and rhythm for the Life Sciences AI organization - driving program planning, status, milestone tracking, and risk management across foundation model research, evaluation, and integration with Lila's closed-loop discovery engine. - Serve as the key communication interface between LSAI and partner", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "seniority": "staff_plus", "years_experience_min": 8, "years_experience_max": 12, "role_function": "product", "employer_industry": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.281364cdab0b456ac7", "query_id": "gq_f4f646ef823e5abb2904", "job_id": "567397ae352ab492504943c885805066", "signal_type": "query_match", "display_text": "Description: \"loop\"", "tooltip": "Description matched \"loop\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior / Principal Technical Program Manager, Life Sciences AI Cambridge, MA USA; San Francisco, CA USA Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), Lila develops large-scale generative models and reasoning frameworks - spanning biological sequences, molecular structures, and multimodal experimental data - that power automated scientific discovery and feed Lila's closed-loop \"Lab-in-the-Loop\" discovery engine. As that work scales, the surface area of cross-functional coordination across model research, evaluation, experimental science, and downstream integration is growing fast. We are looking for a Senior or Principal Technical Program Manager to join our AI Research team and bring structure, accountability, and delivery cadence to Life Sciences AI. You will be the connective tissue between model researchers, computational and wet-lab scientists, automation and software teams, and the broader Lila organization. The successful candidate thrives in ambiguity, communicates exceptionally well across audiences, and knows how to build clarity and momentum on a fast-paced, rapidly scaling team. 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Within Life Science AI (LSAI), Lila develops large-scale generative models and reasoning frameworks - spanning biological sequences, molecular structures, and multimodal experimental data - that power automated scientific discovery and feed Lila's closed-loop \"Lab-in-the-Loop\" discovery engine. As that work scales, the surface area of cross-functional coordination across model research, evaluation, experimental science, and downstream integration is growing fast. We are looking for a Senior or Principal Technical Program Manager to join our AI Research team and bring structure, accountability, and delivery cadence to Life Sciences AI. You will be the connective tissue between model researchers, computational and wet-lab scientists, automation and software teams, and the broader Lila organization. The successful candidate thrives in ambiguity, communicates exceptionally well across audiences, and knows how to build clarity and momentum on a fast-paced, rapidly scaling team. In addition, you must be genuinely curious about how foundation models for biology drive scientific discovery and able to clearly articulate what each program is trying to deliver. 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This role focuses on developing intelligent decisioning systems and building an agent flywheel-a system of feedback loops that continuously evaluate, optimize, and improve agent performance over time. This is an applied AI role with strong data and systems ownership. You will build models and agents and the data pipelines and evaluation loops that enable continuous learning in production. What You'll Do Build the Agent Flywheel Design feedback loops that enable agents and ML systems to improve from real-world outcomes Track outcomes (engagement, conversion, quality) and evaluate agent performance Build pipelines that collect and structure agent traces into training and evaluation datasets Drive continuous improvement via prompting, policies, model selection, and fine-tuning Develop ML & Agent Systems Build and deploy ML models (classification, ranking, forecasting, recommendation) Design AI agents that combine LLM reasoning, tool usage, and ML decisioning Implement reusable patterns for multi-step reasoning, tool orchestration, and structured outputs Integrate models and agents into business-critical workflows Own Data & Model Pipelines Design and build scalable data pipelines (batch and near real-time) for training, evaluation, and inference Transform raw interaction data into features, labels, and evaluation datasets Enable continuous retraining and evaluation through tightly coupled data + model pipelines Ensure data quality, consistency, and reliability Evaluation & Experimentation Build offline and online evaluation frameworks Develop", "parental_leave_weeks": 26, "non_birth_parent_leave_weeks": 12, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://careers.salesforce.com/en/salesforce-stories/wellbeingbenefits/the-benefits-of-parenthood-how-salesforce-supports-growing-families/", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 93, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "seniority": "senior", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "employer_industry": "Technology", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.2bc64d764840fbb963", "query_id": "gq_f4f646ef823e5abb2904", "job_id": "638177b5634a7e7e96fb97075a6c9e30", "signal_type": "query_match", "display_text": "Description: \"loop\"", "tooltip": "Description matched \"loop\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Lead AI Engineer, Data Solutions California - San Francisco; Washington - Seattle; Illinois - Chicago; New York - New York We are looking for a Lead AI Engineer to build next-generation AI and ML systems at Salesforce. This role focuses on developing intelligent decisioning systems and building an agent flywheel-a system of feedback loops that continuously evaluate, optimize, and improve agent performance over time. This is an applied AI role with strong data and systems ownership. You will build models and agents and the data pipelines and evaluation loops that enable continuous learning in production. 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The team partners across Sales, Customer Engineering, Revenue Operations, and GTM AI Operations to translate strategy into field behavior that improves revenue outcomes. As Senior Director, GTM Enablement, you will own the enablement operating system for Pendo's Revenue organization. You will scale Command of the Message reinforcement, build AI-native practice and certification loops, strengthen role-based onboarding, and connect enablement activity to measurable outcomes like ramp time, attainment distribution, pipeline quality, and retention. This role leads a team across role architecture, technical architecture, GTM readiness, and program management, and partners directly with senior GTM leaders to drive decisions that stick. This is a role based in-office in Raleigh, New York, or San Francisco, or Remote in the United States. 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Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: The Autonomous Pilot Integration team for expeditionary platforms builds autonomy solutions for V-BAT - an unmanned aircraft operating in expeditionary environments for ISR, target acquisition, communications, and multi-agent missions. The team also builds autonomy for X-BAT, a new platform under development by the aircraft team; for X-BAT, the team builds on top of the existing mission autonomy solution from the Strategic Systems (ACP) portfolio and works closely with the aircraft team on the flight-autonomy interface. 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This position supports accurate pricing, risk evaluation, and pursuit strategy for treatment facilities, pump stations, lift stations, wells, pipelines, and related systems. ESSENTIAL DUTIES AND RESPONSIBILITIES include the following. Other duties may be assigned. Review proposal specifications and drawings to determine scope of work and required contents of estimate. Prepare complete quantity takeoffs and discipline estimates for water and wastewater construction projects. Develop labor, equipment, material, subcontractor, and indirect cost pricing. Maintain organized estimating files and backup documentation supporting estimate figures and assumptions. Estimate treatment plants, pump stations, pipelines, process/mechanical piping, concrete structures, earthwork, and site utilities. Solicit, review, and level subcontractor and supplier proposals. Review design options and recommend best solutions based on cost, constructability, and schedule impacts. Support estimating and preconstruction teams as required, including assistance on alternate delivery pursuits. COMPETENCIES To perform the job successfully, an individual should demonstrate the following competencies: Planning/Organizing - Prioritizes and plans work activities; uses time efficiently; develops realistic action plans. Innovation - Displays original thinking and creativity; develops innovative estimating approaches. 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