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Required Backend Engineer
About the job Backend Engineer
We are on a mission to rebuild the way justice is being served. Our data-driven platform allows our clients to easily exercise their legal rights by unlocking the access to the court system. We provide an end-to-end solution by combining the technology, funding and expertise needed to resolve any civil case efficiently. We are a fast growing legal tech startup, aiming to help people with their legal disputes by making justice accessible. We are rebuilding the entire way clients handle small legal cases, lowering the bar and reducing friction for $70 billion in case value filed in US courts every year.
We are a group of tech-savvy individuals who are always open to more growth and learning opportunities.
What Youll Do?
Take significant role in our core backend team.
Design & develop new micro services.
Solve complex architecture problems.
Develop infrastructure components to be used across multiple micro services.
Build isomorphic JavaScript packages to be shared across a client & server mono repo.
Use Google Cloud Platform to bundle, build and deploy our products.
What We Offer:
A competitive compensation package of early-stage equity.
Take a major role in the companys success.
Join our experienced highly motivated and fast-moving team.
To do a lot of fun while developing features to improve peoples life.
Our Tech Stack
Node 16, MongoDB Cluster, Cloud Functions, Mono Repo (Lerna), isomorphic JS components, Micro services, Everything is in GCP.

We are looking for highly motivated, unique individuals to join our company for the next phase of our fast moving story.
This Software Engineer role is an opportunity to be one of the first employees to join the team and make a significant impact. Through impeccable software, you will help our customers solve one of the biggest pain points they have encountered, making their life better.
What Youll Do?
Take a significant role in our core backend team.
Design & develop new micro services.
Solve complex architecture problems.
Develop infrastructure components to be used across multiple micro services.
Build isomorphic JavaScript packages to be shared across a client & server mono repo.
You will use Google Cloud Platform to bundle, build and deploy our products.
What We Offer?
A competitive compensation package of early-stage equity.
Take a major role in the companys success.
Join our experienced highly motivated and fast-moving team.
To do a lot of fun while developing features to improve peoples life.
Our Tech Stack
Node 16, MongoDB Cluster, Cloud Functions, Mono Repo (Lerna), isomorphic JS components, Micro services, Everything is in GCP.

We are building the core infrastructure and operating system for autonomous platforms.
Our platform enables real-world autonomous operations by coordinating drones, docking stations, edge systems, operators, and external systems in real time. It serves as the operational backbone for mission execution, command and control, communication, telemetry, orchestration, and fleet coordination across distributed autonomous environments.
This is not a typical backend role.
You will work at the intersection of distributed systems, Command & Control (C2), autonomous platforms, and real-world operations.
We are looking for engineers who are comfortable entering complex and unfamiliar systems, quickly understanding how they work, and turning operational concepts into clean software models and architectures.
You should enjoy reasoning about entities, relationships, state, events, commands, missions, resources, and system boundaries - and designing software that represents and coordinates them reliably.
The platform includes:
Command & Control capabilities for autonomous systems
Fleet orchestration and mission lifecycle management
Real-time communication between distributed autonomous components
Modeling and management of operational entities, states, and relationships
Telemetry, video, and operational data processing
Device interoperability across heterogeneous autonomous platforms
SDKs and APIs for platform integration and extensibility
AI agents and intelligent automation interacting with operational systems
As a Senior Engineer, you will help shape the technical foundation and domain architecture of the platform while solving complex problems across distributed systems, autonomous operations, and Command & Control.
Responsibilities
Design and build software systems for autonomous platform operations and Command & Control
Translate complex operational requirements and domain concepts into robust software architectures
Model operational entities, relationships, state transitions, commands, events, and workflows
Lead architecture and system design decisions across services, communication layers, and domain boundaries
Build scalable, low-latency, resilient production services
Design reliable communication and messaging flows between distributed system components
Solve complex challenges involving concurrency, scalability, latency, synchronization, and fault tolerance
Build abstraction layers for integrating heterogeneous autonomous devices, platforms, and external systems
Develop internal SDKs, APIs, and reusable infrastructure components
Design systems that can be consumed and operated by humans, autonomous components, and AI agents
Improve observability, debugging capabilities, and operational reliability
Drive engineering best practices, clean architecture, and code quality standards
Explore new technologies and approaches when existing solutions are not sufficient

we are a leading company specializing in the development and manufacturing of autonomous drone systems. Its solutions combine hardware, software, and AI-driven technologies to support complex, large-scale operations. our company works primarily with defense industries, such as military and law enforcement agencies worldwide. The company is part of Ondas Holdings and operates as a wholly owned subsidiary.
We are looking for an experienced Technical Writer.
Responsibilities
Write, update, and maintain technical and operational documentation, including manuals, procedures, work instructions, user guides, and maintenance documentation.
Write, edit, format, and organize documents in Microsoft Word according to defined templates, style guides, and documentation standards.
Update and modify diagrams, process flows, and flowcharts using Microsoft Visio.
Collect, analyze, and structure technical information from engineering teams, product teams, and subject matter experts.
Collaborate with technical teams and SMEs to create, review, validate, and update documentation.
Ensure documentation is accurate, clear, consistent, and suitable for the intended audience.

We are looking for a Head of RL Gym to own the end-to-end delivery, quality, and scaling of RL Gym programs for our strategic AI partners.
You will work closely with frontier-model teams, Delivery, Product, Sales, Research, and Operations to turn complex customer and model-development needs into high-quality, scalable programs.
What Youll Own
Own partner delivery from scoping through launch, execution, scaling, and iteration.
Translate research and model needs into clear program requirements, workflows, and success metrics.
Work closely with frontier-model teams to identify model gaps and design RL Gym programs that address them.
Own quality standards and quality assurance, ensuring consistent, reliable outputs across programs.
Build the processes, systems, and operating model required to scale programs efficiently without compromising quality.
Partner with Sales on customer discovery, solution design, scoping, and expansion opportunities.
Work closely with Product to translate partner needs and operational learnings into product requirements and roadmap priorities.
Build strong feedback loops across customers, model teams, Sales, Product, and Operations.
Own operational performance across quality, capacity, timelines, cost, throughput, and partner satisfaction.
Build and lead the program management and operations function as the business scales.

We're looking for an exceptional, hands-on technical leader to join our core engineering team as a true force multiplier and technological anchor for the company. You'll have the flexibility to drive high-impact initiatives from conception to production, own complex technical and AI domains end-to-end, or guide and level up a focused delivery team, operating with a high degree of autonomy in an ambiguous, fast-moving environment.
What You'll Do:
Drive strategic initiatives: lead complex technical projects, AI feature developments, and core architectural components with an ownership mindset.
Architect for AI & data at scale: design robust, scalable infrastructure and pipelines capable of handling massive volumes of cloud infrastructure data.
Bridge product, AI & tech: collaborate closely with Product Managers and data specialists to translate business roadmaps into high-performance technical systems.
Promote engineering excellence: establish development guidelines, lead design and architecture reviews, and raise the bar for code quality across teams.
Mentor & guide: act as a technical mentor to fellow engineers through hands-on collaboration, deep code reviews, and architectural consultation.

our company's RL Gyms team builds adversarial environments and evaluations that the world's leading AI labs use to test and train their most capable agents. Every engagement is a delivery project: a lab specifies what it needs to measure, and we produce the environments, adversarial tasks, and verifiers that measure it, against a deadline and a quality bar the customer sets.
Our customers are the frontier labs. You'll work alongside the people building the models everyone else is reading about, on the problems they haven't solved yet, and what you ship becomes part of how they decide whether an agent is safe to release. Very few roles put you that close to the frontier.
We're looking for a Technical Project Manager to own those projects. You'll run several lab engagements at once, each with its own researchers, engineers, and reviewers. You decide who works on what, you hold the schedule, and you decide whether a batch is good enough to ship.
The field is cutting edge and the projects change shape faster than any playbook can describe.
What you'll do
Project ownership
Own concurrent lab engagements end-to-end: scope, milestones, delivery, and status
Turn a customer specification into a task plan with your researchers and engineers: how many tasks, in which environments, against which models, by when
Track every batch through the pipeline (in progress, under review, approved, delivered) and keep the delivery record accurate enough that anyone can read the state of a project in a minute
Surface a slipping deadline early, with the tradeoff already worked out: cut scope, add capacity, or move the date
Researcher allocation and quality
Allocate researchers, task writers, and reviewers across projects based on throughput and specialty
Own their onboarding: brief them on the environment, the attack pattern, and the acceptance criteria, then get them to their first accepted task fast
Give quality feedback on rejected work and spot the patterns behind repeat rejections
Manage the review chain so that no batch reaches the customer before it has cleared internal review.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We're looking for an exceptional, hands-on leader-engineer to join our core engineering team as a true force multiplier and technological anchor for the company. You'll have the flexibility to drive high-impact initiatives from conception to production, own complex technical and AI domains end-to-end, or guide and level up a focused delivery team, operating with a high degree of autonomy in an ambiguous, fast-moving environment.
What You'll Do:
Drive strategic initiatives: lead complex technical projects, AI feature developments, and core architectural components with an ownership mindset.
Architect for AI & data at scale: design robust, scalable infrastructure and pipelines capable of handling massive volumes of cloud infrastructure data.
Bridge product, AI & tech: collaborate closely with Product Managers and data specialists to translate business roadmaps into high-performance technical systems.
Promote engineering excellence: establish development guidelines, lead design and architecture reviews, and raise the bar for code quality across teams.
Mentor & guide: act as a technical mentor to fellow engineers through hands-on collaboration, deep code reviews, and architectural consultation.

We're looking for an AI researcher to take an integral part in research into how these models behave, how they should be evaluated, and how they can be deployed and used more efficiently across real engineering workloads.
This is a deeply technical research role with direct product impact.
You'll study frontier and open-source models, develop evaluation frameworks, investigate inference and model optimization techniques, explore local deployment strategies, and help answer questions such as:
Which model should handle a particular task?
How much capability do we lose when we quantize it?
Can a smaller local model replace a frontier API for a specific workload?
How should models be routed across latency, cost, privacy, and quality constraints?
How do we measure whether one model is actually better than another for agentic software engineering?
Your work will directly shape model strategy and the intelligence behind our AI infrastructure products.

we are looking for a Senior Software Engineer - Endpoint.
What You'll Do:
Own the installation and enrollment mechanics, the CLI flows, MDM scripts, and platform-specific packaging (Windows msi/exe, macOS pkg, Linux deb/rpm) that put TokenShift on developer machines at fleet scale.
Instrument and monitor endpoint behavior through the telemetry pipeline, using signals to improve coverage and catch regressions.
Build and maintain a robust lab enabling automatic research and development of new capabilities and platforms by AI agents.
Build and maintain agent harnesses, the per-agent adapters that hook into Claude Code, Cursor, Copilot CLI, Codex, and future agents to route network and shell commands through the TokenShift proxy.
Develop and extend compression rules and strategies for new command types (git, build tools, infra CLIs, search, and more), ensuring token reduction never comes at the cost of semantic accuracy.
Maintain correctness and resilience of the proxy layer, compression must be reversible on demand, hooks must never block a developer's command, and failures must be silent and transparent.
Collaborate with the research team to translate newly supported agent architectures into production-grade harnesses.

We're looking for a Senior Product Manager to own TokenShift, PointFive's product for finding and cutting wasted AI token spend. This is a chance to define product strategy for a brand-new category, working hands-on with R&D on how AI agents and LLM models actually run, get monitored, and get paid for in production. You'll take TokenShift from early adoption to a core pillar of PointFive's platform, alongside our flagship DeepWaste product.
What You'll Do:
Own the product strategy and roadmap for TokenShift, from discovery through GA and beyond.
Translate a deep technical understanding of how AI agents and LLM workloads run, inference calls, endpoints, agent harnesses and orchestration, into requirements R&D can build against.
Partner with R&D, design, and data science to scope, prioritize, and ship features that surface and reduce AI token waste.
Work directly with enterprise customers and prospects to understand how they deploy AI agents in production and where their cost and visibility gaps are.
Define and track success metrics, adoption, cost savings, detection accuracy, and use them to drive roadmap decisions.
Collaborate with go-to-market teams on positioning, messaging, and launch plans for new capabilities.
Stay current on the landscape: coding agents and harnesses, LLM gateways and routers, AI observability, and endpoint management.
Represent the voice of the customer internally, balancing technical depth with business impact.