Machine Learning Engineer - Speech & Natural Language

About Primordial Labs 

We're building the next generation interface for commanding autonomous systems in the real world. Our core product, Anura, translates spoken or typed intent into structured actions across drones, sensors, and mission software—enabling faster, more intuitive control in high-stakes environments. We’re a distributed team of engineers, operators, and technologists focused on deploying reliable autonomy at the tactical edge.

The Opportunity

Anura turns a warfighter's spoken intent into robotic action, and it does it on hardware carried into the field, with no cloud connection and no second chances. That constraint is the entire job.

You own the models that make natural language work at the tactical edge: speech recognition, natural language understanding, and the full path from candidate approach to a trained, hardened model running reliably on constrained compute.

This is not a foundational research role. We are building the most accurate and lowest-latency language stack that will fit in a soldier's ruck, and we're looking for someone who has already done work like this and can show it.

What You'll Do

• Evaluate and Decide: Assess candidate architectures and modeling approaches against the criteria that actually matter operationally: accuracy under degraded conditions, latency, and footprint. Make the call and defend it with data, not vibes.

• Build the Data Engine: Develop synthetic data generation tooling and augmentation pipelines, and curate the data we collect from the field. Our accuracy advantage comes from data we build ourselves, and you'll own that machinery.

• Train for the Domain: Train and fine-tune models that understand how operators actually talk, in noisy environments and with degraded audio, using language no general-purpose model has seen enough of.

• Optimize Ruthlessly: Every millisecond of latency and every megabyte of memory is a design decision with operational consequences. You'll find the wins and prove them with benchmarks.

• Mature the Deployment: Turn research-grade code into something we can ship: model weights served directly through an efficient production runtime, with the experimental scaffolding stripped out.

• Close the Loop: Own how we measure success. Build evaluation that reflects whether an operator's command actually worked, not just how a model scores on a test set, and turn field failures into test cases, training data, and fixes that stick.

• Sharpen Our MLOps: Improve how we train, track, evaluate, and release models. At minimum, be relentlessly vocal about what's broken.

About You

• Proven, Not Learning on the Job: You've shipped ML into production and can walk through exactly what you built, what broke, and what you measured. This role needs real ownership from day one.

• Deployment-Minded: A model that only runs in a notebook isn't finished. You think about memory ceilings, inference paths, and startup time as naturally as you think about loss curves.

• Data-Obsessed: You know that improving the data usually beats tuning the architecture, and when the data you need doesn't exist, your instinct is to build the tooling to create it rather than wait on someone else.

• Language and Speech Native: Speech recognition and language understanding are your home turf. You have opinions about decoding strategies and about the ways standard accuracy metrics mislead you.

• Pragmatist Over Purist: You reach for the smallest thing that solves the problem, and you can tell the difference between a model that benchmarks well and one that works for a user under pressure.

• Remote-Ready: You communicate clearly in writing, work independently across time zones, and keep the team synced without being asked.

Required Qualifications

• Track Record: Substantial hands-on experience training, optimizing, and deploying ML models in production systems. We're flexible on years and title, biased toward senior, and inflexible on demonstrated results.

• Speech and Language Depth: Deep, hands-on experience across both speech recognition and natural language understanding, including adapting models to a specialized domain.

• Core Skills: Deep proficiency in Python and modern deep learning frameworks, including custom training loops, data pipelines, and evaluation harnesses.

• Optimization: Demonstrated experience making models meaningfully faster or smaller, with the benchmarks to back it up.

• Productionization: Hands-on work moving models out of framework and research code into an efficient, deployable inference path.

• Education: Degree in CS, EE, Computational Linguistics, or a related technical field, or equivalent proof you can do the work.

Desirable Qualities

• Deploying models on embedded or resource-constrained GPU hardware

• Defense, robotics, or autonomy experience

• Model lifecycle tooling: experiment tracking, model registries, automated evaluation

Important Qualification Note: Due to the nature of our work and federal compliance requirements, this position strictly requires U.S. Citizenship and current residency within the United States. Please note that Permanent Residency (Green Card status) or visa sponsorship cannot satisfy these specific regulatory requirements.

Compensation

Compensation for this position includes a target base salary of $200,000 - $275,000 annually, with the specific figure determined by the candidate's experience level.

We’re committed to fair and equitable compensation and are happy to explain how we determine our salary ranges, just ask!

Our Benefits 

• Beyond a Competitive Salary, we provide a significant equity stake, aligning your growth with ours.

• 100% Remote: This role is fully remote, open to residents across the US. We believe that talent knows no borders (well, except for the US in this case!).

• Comprehensive Health Coverage: We provide comprehensive medical, dental, and vision plans so you and your family can stay healthy and worry free.

• Retirement Planning: Plan for tomorrow with our 401(k) package, complete with a generous 6% company match.

• Generous DTO: Our generous Discretionary Time off (DTO) policy is all about helping you take the breaks you deserve and live your healthiest, most fulfilling life.

• Elite Technology Package: Enjoy an elite technology package to make your work effortless and efficient.

Our Interview Process

Our interview process is designed to be thorough and respectful of your time. It typically includes:

1. Conversation with the People Team: Lets get to know one another first!

2. Screener Interview: A 30-minute discussion about relevant engineering concepts and your past experience.

3. Offline Coding Test: We provide an at your own pace test focused on algorithm and system design. After completion, you'll present your solution in a follow-up call. This allows us to understand your thought processes and technical approach.

4. Technical Collaboration & Team Q&A: Walk through a recent code sample with the engineering team, discuss your design choices, and ask us anything about our stack, culture, and day to day work.

We are committed to a low-stress interview experience—no trick questions or brain teasers!

Primordial Labs is an equal opportunity employer, committed to creating a welcoming and respectful environment for everyone. Discrimination and harassment have no place here. We firmly believe that a diverse team enriches our problem-solving capabilities, sparks greater creativity, and ultimately leads to a better product and a more vibrant company.

We understand that great talent comes from various paths and that passion and transferable skills are invaluable. At Primordial Labs, what truly matters is your ability to make an impact, your dedication to our mission, and your capacity to thrive in a fast-paced, collaborative atmosphere. We are dedicated to fostering an inclusive workplace where you can contribute your unique talents, expand your skills, and learn continuously. If you’re driven to make a real impact, we encourage you to apply, even if your experience doesn’t perfectly align with every qualification.

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