Safety Professional 1 - Monroe, LA

About M.C. Dean M.C. Dean is Building Intelligence. We design, build, operate, and maintain cyber-physical solutions for the nation’s most mission-critical facilities, secure environments, complex infrastructure, and global enterprises. With over 7,000 employees, our capabilities span electrical, electronic security, telecommunications, life safety, automation and controls, audiovisual, and IT systems. Headquarters in Tysons, Virginia, M.C. Dean delivers resilient, secure, and innovative power and technology solutions through engineering expertise and smart systems integration. Why Join Us? Our people are passionate about engineering innovation that improves lives and drives impactful change. Guided by our core values—agility, expertise, and trust—we foster a collaborative and forward-thinking work environment. At M.C. Dean, we are committed to building the next generation of technical leaders in electrical, engineering, and cybersecurity industries. Responsibilities The Safety Professional is responsible for oversight of Construction Project Safety, Health (S&H), and Risk Management, effectively managing multiple Safety programs, developing and implementing M.C. Dean site-specific project safety plans, and collaborating with Project Management in the implementation and leadership of the Operational Risk Management (ORM) process. Provides solutions to a wide range of difficult problems. Solutions are imaginative, thorough and practical. Coordinate with General Contractor (GC) and/or owner of general site conditions and/or Non-Compliance Notice (NCN) Participate in meetings with customers, project team members, and contractors/sub-contractors to advise of project-specific mitigation plans, S&H training programs, and technical reports Assist with the development and implementation of Site Specific Accident Prevention Programs (APP) and training plans to meet project safety plan specs and requirements Assist with site safety budgets, necessary safe work permits and assure compliance with project contract, plans, and specifications Lead the Operational Risk Management (ORM) team in reviewing, interpreting, and developing a project safety plan based on project, client/owner, Program Manager, and General Contractor (GC) specifications, design drawings, regulations, and existing safety system - Conduct / assist with incident / investigation reporting. Knowledge of safety and health standards, rules, regulations - Working knowledge of safe work practices, accident investigation techniques, Occupational Safety & Health Administration (OSHA), U.S. Department of Transportation (DOT) compliance, insurance audits, workers compensation, project health surveys, Activity Hazard Analysis (AHA), site emergency evacuation and first aid plans Proficiency in Microsoft (MS) Office suite, including Share Point Position / Candidate Requirements: 6 years with a high school diploma 4 years with an Associates Degree 0 years with a Bachelors Degree Bachelor's degree preferred Experience conducting S&H training, Regulatory compliance expert/1910, 1926, EM-385 1-1 Safety Management Systems knowledge, skill and experience ANSI-Z10, OHSAS 18001, ISO 3100 Understanding of culture and culture building skills Trainer Designation - OSHA 500/502 - Safety and Health / Occupational Safety degree from an ABET accredited program Willingness to pursue Associate Safety Professional (ASP), Certified Safety Professional (CSP), Construction Health and Safety Technician (CHST) Previous electrical / construction / industrial /federal and commercial S&H experience What we offer: A collaborative team inspired by the way engineering and innovation enhance customer outcomes, improve lives, and change the world for the better. We are driven by our core values of agility, expertise, and trust. An opportunity to lead and build a business with the support of an industry-leading firm that has been in business for 75 years. Investment in your skills and expertise through a combination of professional and technical training programs, including leadership training and tuition reimbursement. Open and transparent communication with senior leadership as well as local office management. We offer an excellent benefits package including: A competitive salary Medical, dental, vision, life, and disability insurance Paid time off Tuition reimbursement 401k Retirement Plan Military Reserve pay offset Paid maternity leave Abilities: Exposure to computer screens for an extended period of time. Sitting for extended periods of time. Reach by extending hands or arms in any direction. Have finger dexterity in order to manipulate objects with fingers rather than whole hands or arms, for example, using a keyboard. Listen to and understand information and ideas presented through spoken words and sentences. Communicate information and ideas in speaking so others will understand. Read and understand information and ideas presented in writing. Apply general rules to specific problems to produce answers that make sense. Identify and understand the speech of another person.

Software Engineering Intern

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair. Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. What makes someone good at this Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start. Good fit if you: Are graduating in 2026 or 2027 Can code in Python Are confident working independently Are motivated by problems that require both technical skill and creative cleverness No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users Prefer a highly collaborative team environment with shared ownership Want extensive structured mentorship This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include 401k, health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Software Engineering Intern

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair. Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. What makes someone good at this Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start. Good fit if you: Are graduating in 2026 or 2027 Can code in Python Are confident working independently Are motivated by problems that require both technical skill and creative cleverness No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users Prefer a highly collaborative team environment with shared ownership Want extensive structured mentorship This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include 401k, health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Senior Software Engineer

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks, owning the full lifecycle from ideation through grading, failure analysis, and iteration. At this level, we expect you to work on our most complex tasks: environments involving multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, or challenging system design problems. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. You will also contribute to shared infrastructure and tooling, and may take on mentorship responsibilities for newer team members. What makes someone good at this Deep software engineering experience across multiple domains, combined with a strong intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and design tasks that target deeper, more subtle failure modes from areas you know well: infrastructure, distributed systems, performance, security, or other specializations. Good fit if you: Have deep expertise in at least one area of software engineering Can code in Python Are confident working independently on complex, ambiguous problems Have extensive experience working with coding agents No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users This is independent, high-ownership work. You own your tasks from start to finish, with regular feedback. Strong performers are recognized and rewarded. Benefits include health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Software Engineering Intern

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair. Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. What makes someone good at this Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start. Good fit if you: Are graduating in 2026 or 2027 Can code in Python Are confident working independently Are motivated by problems that require both technical skill and creative cleverness No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users Prefer a highly collaborative team environment with shared ownership Want extensive structured mentorship This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include 401k, health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Senior Software Engineer

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks, owning the full lifecycle from ideation through grading, failure analysis, and iteration. At this level, we expect you to work on our most complex tasks: environments involving multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, or challenging system design problems. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. You will also contribute to shared infrastructure and tooling, and may take on mentorship responsibilities for newer team members. What makes someone good at this Deep software engineering experience across multiple domains, combined with a strong intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and design tasks that target deeper, more subtle failure modes from areas you know well: infrastructure, distributed systems, performance, security, or other specializations. Good fit if you: Have deep expertise in at least one area of software engineering Can code in Python Are confident working independently on complex, ambiguous problems Have extensive experience working with coding agents No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users This is independent, high-ownership work. You own your tasks from start to finish, with regular feedback. Strong performers are recognized and rewarded. Benefits include health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Junior Software Engineer

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair. Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. What makes someone good at this Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start. Good fit if you: Have just graduated or are about to graduate Can code in Python Are confident working independently Are motivated by problems that require both technical skill and creative cleverness No prior ML or AI experience required We're happy to hire candidates who are very capable, even if they have no prior professional software experience. Probably not a good fit if you: Want a product engineering role building features for end users Prefer a highly collaborative team environment with shared ownership Want extensive structured mentorship This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Senior Software Engineer

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks, owning the full lifecycle from ideation through grading, failure analysis, and iteration. At this level, we expect you to work on our most complex tasks: environments involving multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, or challenging system design problems. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. You will also contribute to shared infrastructure and tooling, and may take on mentorship responsibilities for newer team members. What makes someone good at this Deep software engineering experience across multiple domains, combined with a strong intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and design tasks that target deeper, more subtle failure modes from areas you know well: infrastructure, distributed systems, performance, security, or other specializations. Good fit if you: Have deep expertise in at least one area of software engineering Can code in Python Are confident working independently on complex, ambiguous problems Have extensive experience working with coding agents No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users This is independent, high-ownership work. You own your tasks from start to finish, with regular feedback. Strong performers are recognized and rewarded. Benefits include health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.

Software Engineering Intern

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve. What you'll do You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair. Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways. What makes someone good at this Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start. Good fit if you: Are graduating in 2026 or 2027 Can code in Python Are confident working independently Are motivated by problems that require both technical skill and creative cleverness No prior ML or AI experience required Probably not a good fit if you: Want a product engineering role building features for end users Prefer a highly collaborative team environment with shared ownership Want extensive structured mentorship This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include 401k, health, dental, vision, and life insurance. Applying takes less than one minute. Interview process: https://www.mechanize.work/how-our-interview-process-works Learn more about the work: https://www.mechanize.work/what-working-here-is-like About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.