Associate Member of Technical Staff Jobs
Associate Member of Technical Staff jobs are open across semiconductors, enterprise software, cloud infrastructure, and research labs, from new-grad to mid-level, with specializations in systems engineering, embedded software, and applied research. Find a role that fits below and apply directly.
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Showing 5 of 187+ Associate Member of Technical Staff jobs
About Hark
Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.
We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.
To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.
About the Role
At Hark, you'll lead the development for the foundation of agentic reinforcement learning (RL) environments: the digital worlds in which AI agents learn to use software, navigate complex workflows, and act on their surroundings. Our ambition is to simulate the breadth and complexity of the internet, connecting websites, apps, services, and artifacts into rich, stateful environments, with the potential to evolve dynamically over time.
This is a chance to shape both the worlds agents learn in and the systems that make learning at scale possible. You'll build environments end-to-end and work closely with researchers to bring them into RL training and evaluation. You'll also develop the infrastructure behind our agent gym, making these environments easy to integrate, reproduce, and scale across heterogeneous execution targets.
Responsibilities
- Build agent environments and simulations end-to-end, including frontend interfaces, backend services, APIs, data models, tools, and realistic workflows used to train and evaluate AI agents.
- Build the infrastructure that powers our agent gym, including orchestration, sandboxing, packaging, benchmarking, and systems for running environments across heterogeneous targets.
- Create interconnected simulations with coherent behavior and shared state across websites, apps, and artifacts. Explore dynamic environments with changing content, background activity, and events over time.
- Partner with researchers to integrate environments into RL training and evaluation pipelines, building reusable agent interfaces, environment lifecycle APIs, and reliable collection of trajectories and artifacts.
- Build infrastructure that supports thousands of concurrent sandboxed environments, with isolation between episodes, consistent state, snapshots and resets, and recovery from partial failures.
- Improve startup and teardown latency, scheduling, resource utilization, and throughput to make large-scale RL rollouts efficient and cost-effective.
- Build observability, debugging, and reproducibility tools. Diagnose resource contention, timeouts, and runtime failures, and distinguish infrastructure problems from agent behavior.
Requirements
- Strong full-stack engineering skills, with experience building applications across frontend interfaces, backend services, APIs, and data models.
- Experience with stateful backend or distributed systems, including databases, queues, caching, concurrency, consistency, and failure recovery.
- Strong algorithms and systems fundamentals. You can profile complex workloads, identify bottlenecks, and validate performance improvements without compromising correctness.
- Ability to design clear, reusable APIs and abstractions that make complex environments easy for researchers and engineers to integrate, extend, and operate.
- Careful debugging and testing habits, with attention to application fidelity, state transitions, reproducibility, and failures that can silently corrupt results.
- Creativity and ownership in ambiguous problems. You can turn real software behavior into working simulations, collaborate closely with researchers, and learn the agent and RL systems your environments support.
Bonus Qualifications
- Experience integrating environments with RL rollout systems, agent training pipelines, or evaluation harnesses.
- Experience with containers, virtual machines, sandbox runtimes, or orchestration systems for large-scale distributed workloads.
- Experience with browser automation, computer-use agents, simulation frameworks, or dynamic systems driven by events.
Compensation
The US base salary range for this full-time position is between $170,000 - $400,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components and benefits depending on the specific role. This information will be shared if an employment offer is extended.
About Hark
Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.
We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.
To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.
About the Role
At Hark, you'll lead the development for the foundation of agentic reinforcement learning (RL) environments: the digital worlds in which AI agents learn to use software, navigate complex workflows, and act on their surroundings. Our ambition is to simulate the breadth and complexity of the internet, connecting websites, apps, services, and artifacts into rich, stateful environments, with the potential to evolve dynamically over time.
This is a chance to shape both the worlds agents learn in and the systems that make learning at scale possible. You'll build environments end-to-end and work closely with researchers to bring them into RL training and evaluation. You'll also develop the infrastructure behind our agent gym, making these environments easy to integrate, reproduce, and scale across heterogeneous execution targets.
Responsibilities
- Build agent environments and simulations end-to-end, including frontend interfaces, backend services, APIs, data models, tools, and realistic workflows used to train and evaluate AI agents.
- Build the infrastructure that powers our agent gym, including orchestration, sandboxing, packaging, benchmarking, and systems for running environments across heterogeneous targets.
- Create interconnected simulations with coherent behavior and shared state across websites, apps, and artifacts. Explore dynamic environments with changing content, background activity, and events over time.
- Partner with researchers to integrate environments into RL training and evaluation pipelines, building reusable agent interfaces, environment lifecycle APIs, and reliable collection of trajectories and artifacts.
- Build infrastructure that supports thousands of concurrent sandboxed environments, with isolation between episodes, consistent state, snapshots and resets, and recovery from partial failures.
- Improve startup and teardown latency, scheduling, resource utilization, and throughput to make large-scale RL rollouts efficient and cost-effective.
- Build observability, debugging, and reproducibility tools. Diagnose resource contention, timeouts, and runtime failures, and distinguish infrastructure problems from agent behavior.
Requirements
- Strong full-stack engineering skills, with experience building applications across frontend interfaces, backend services, APIs, and data models.
- Experience with stateful backend or distributed systems, including databases, queues, caching, concurrency, consistency, and failure recovery.
- Strong algorithms and systems fundamentals. You can profile complex workloads, identify bottlenecks, and validate performance improvements without compromising correctness.
- Ability to design clear, reusable APIs and abstractions that make complex environments easy for researchers and engineers to integrate, extend, and operate.
- Careful debugging and testing habits, with attention to application fidelity, state transitions, reproducibility, and failures that can silently corrupt results.
- Creativity and ownership in ambiguous problems. You can turn real software behavior into working simulations, collaborate closely with researchers, and learn the agent and RL systems your environments support.
Bonus Qualifications
- Experience integrating environments with RL rollout systems, agent training pipelines, or evaluation harnesses.
- Experience with containers, virtual machines, sandbox runtimes, or orchestration systems for large-scale distributed workloads.
- Experience with browser automation, computer-use agents, simulation frameworks, or dynamic systems driven by events.
Compensation
The US base salary range for this full-time position is between $170,000 - $400,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components and benefits depending on the specific role. This information will be shared if an employment offer is extended.
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Find JobsAssociate Member of Technical Staff Job Market
Who's Hiring
- Perplexity20
- Amazon11
- micro19
- Reflection AI7
- deCircle6
Top Industries Hiring
- Technology & Software34
- Artificial Intelligence9
- Science & Research3
- Electronics & Hardware3
- Retail2
What Employers Look For
The qualifications that appear most often in associate member of technical staff jobs.
- Bachelor's or master's degree in computer science, electrical engineering, or a closely related technical field
- Proficiency in at least one systems or application programming language such as C++, Python, or Java
- Experience with version control systems and collaborative development workflows
- Ability to read, write, and review technical design documents and architecture specifications
- Familiarity with debugging tools, performance profiling, and unit testing frameworks
- Strong written and verbal communication skills for cross-functional engineering collaboration
Tips for Your Associate Member of Technical Staff Job Search
Tailor your resume to the team
Associate member of technical staff roles vary sharply between hardware teams, software organizations, and research groups. Mirror the exact technical vocabulary in each job posting, whether that is RTL design, distributed systems, or ML infrastructure, so your resume passes automated screening.
Show project scope, not just tasks
Hiring panels for this role want to see what you built or improved end to end. Replace duty-focused bullet points with outcomes: what the system did before you touched it, what changed, and how that was measured or validated.
Apply early to roles that fit
Migrate Mate lists associate member of technical staff openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that match your stack
Many associate member of technical staff openings are written for a specific environment, such as CUDA, RISC-V, or Kubernetes. Applying only where your core tools appear in the first three bullet points of the requirements section dramatically improves your callback rate.
Prepare a whiteboard session independently
Most associate member of technical staff interviews include a live coding or system design exercise that differs from general software engineer rounds. Practice talking through your design decisions out loud, not just arriving at the answer, since interviewers score your reasoning as heavily as your solution.
Negotiate scope alongside compensation
At the associate level, project assignment often matters more than base salary for long-term growth. When you reach the offer stage, ask directly which team you would join and what your first project would be, then use that detail to compare offers across companies.
Associate Member of Technical Staff Jobs: Frequently Asked Questions
Which companies are hiring the most associate member of technical staffs?
The most active employers for associate member of technical staffs right now are Perplexity, Amazon, and micro1, and the most openings are in California, New York, and Washington, based on current listings on Migrate Mate as of November 2026. Demand is concentrated at large semiconductor manufacturers, cloud platform providers, and enterprise infrastructure companies.
How many associate member of technical staff jobs are remote?
About 41% of associate member of technical staff openings are fully remote or hybrid as of November 2026, though availability varies by specialization. Software-focused and applied research sub-roles tend to have the highest remote share, while hardware verification, lab-based embedded work, and on-chip design roles are more likely to require on-site presence.
How do you become a associate member of technical staff?
Most associate member of technical staff roles require completing a bachelor's or master's degree in a technical discipline, then building hands-on experience through internships, research projects, or open-source contributions that demonstrate you can work within a production or research codebase. From there, you apply to entry-level or associate-level postings, pass a technical screening and design interview, and join a team where you contribute to real systems under senior guidance.
Can I get an associate member of technical staff job with little experience?
Yes, the associate level is specifically designed for candidates early in their careers, so employers expect limited professional experience. What matters most is a strong academic project, a published research contribution, or an internship where you shipped something real. Candidates who can walk an interviewer through a concrete technical problem they solved, even from a class or personal project, are competitive for these roles.
What does the associate member of technical staff interview process look like?
The process typically starts with a recruiter screen, followed by a technical phone interview covering coding fundamentals or domain-specific concepts. That leads to a full interview loop, which usually includes a live coding or whiteboard exercise, a system design or architecture discussion scaled to the associate level, and a behavioral round with the hiring manager or team lead. Some organizations add a take-home assignment before the final loop.
Where can I find and apply to associate member of technical staff jobs?
You can find and apply to associate member of technical staff jobs on Migrate Mate, which lists current openings from across the United States in one place. Find the roles that match your background and apply directly to each listing.
See All 187+ Associate Member of Technical Staff Jobs
Find roles that match your experience and apply in just a few clicks.
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