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About This Role
About Tacit
We are an early\-stage, deep tech startup based in San Francisco, developing innovative hardware that rethinks human\-computer interaction. We are backed by General Catalyst, Khosla Ventures, and Greylock Partners, with a founding team from Stanford, BrainGate, Oculus, and Tesla. While we can’t reveal too much just yet, our team is tackling cutting\-edge engineering challenges to bring revolutionary products to life.
About the role
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We’re looking for a Senior Firmware Engineer, Edge AI / NPU Runtime to help architect, optimize, and ship next\-generation neurotech hardware with production\-grade on\-device intelligence. You will own critical parts of the embedded AI stack, from realtime sensor acquisition through preprocessing, NPU/DSP\-accelerated inference, postprocessing, telemetry, and product deployment.
This is a hands\-on role for someone who wants to work close to the hardware while shaping the intelligence users experience in the product. You’ll help define how models run on\-device, how sensor data moves through the system, and how we meet tight latency, reliability, and power budgets in real\-world use.
What you'll do
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- Edge AI \& NPU Inference
+ Own deployment of ML models onto embedded targets using NPUs, DSPs, MCUs, or other hardware accelerators.
+ Integrate embedded inference runtimes, vendor NPU/DSP SDKs, and model deployment workflows into production firmware.
+ Optimize inference latency, memory footprint, throughput, power consumption, and accelerator utilization on production hardware.
+ Partner with ML teams on quantization, operator support, model architecture tradeoffs, calibration datasets, and accuracy/performance regressions.
- Realtime Sensor\-to\-Inference Systems
+ Build realtime sensor\-to\-inference pipelines, including acquisition, timestamping, synchronization, preprocessing, feature extraction, model execution, and postprocessing.
+ Design low\-latency data movement using DMA, interrupts, ring buffers, deterministic scheduling, and efficient memory layouts.
+ Support streaming inference patterns such as sliding windows, temporal models, event\-driven execution, and continuous sensor processing.
+ Maintain inference quality and timing guarantees under real\-world conditions such as sensor noise, clock drift, dropped samples, variable system load, and power\-state transitions.
- Power\-Optimized Embedded Firmware
+ Optimize end\-to\-end energy per inference across sensing, preprocessing, model execution, postprocessing, and idle time.
+ Use low\-power firmware techniques such as sleep states, duty cycling, subsystem power gating, clock scaling, batching/windowing, and dynamic power management.
+ Profile and improve power consumption across sensors, CPU, NPU/DSP, memory, and supporting firmware infrastructure.
- Product Quality \& Debugging
+ Bring up and debug firmware across sensors, accelerators, power systems, embedded compute, and production hardware.
+ Use lab tools, traces, logs, telemetry, and instrumentation to root\-cause complex embedded system issues.
+ Translate product and customer experience goals into concrete latency, reliability, responsiveness, and power targets.
+ Build diagnostics, validation hooks, and performance benchmarks to ensure reliable real\-world edge inference behavior.
Requirements
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- 5\+ years of experience in embedded firmware, embedded systems, or edge ML systems.
- Strong C/C\+\+/Rust experience on resource\-constrained embedded platforms.
- Experience with RTOS\-based systems such as FreeRTOS, Zephyr, ThreadX, or similar.
- Experience deploying or optimizing ML inference on embedded targets, NPUs, DSPs, MCUs, or edge SoCs.
- Strong understanding of realtime embedded systems, including DMA, interrupts, concurrency, memory management, and low\-latency data movement.
- Experience optimizing embedded systems for latency, memory footprint, throughput, and power consumption.
- Hands\-on debugging and bring\-up experience across embedded hardware and firmware systems, with strong cross\-functional communication across firmware, ML, electrical, software, and product teams.
Strong candidates may have
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- Experience with embedded inference runtimes, deployment toolchains, or edge AI SoCs/accelerators such as TensorFlow Lite Micro, ONNX Runtime, CMSIS\-NN, Qualcomm QNN/SNPE, ARM Ethos\-U/Vela, TVM, ExecuTorch, Qualcomm, ARM, Cadence/Tensilica, Syntiant, Ambiq, Nordic, NXP, ST, Hailo, Google Edge TPU, or similar.
- Experience with quantized inference, fixed\-point math, SIMD/DSP optimization, accelerator programming, or model conversion workflows.
- Experience with streaming or time\-series ML workloads such as biosignals, sensor fusion, audio, gesture recognition, keyword spotting, or other realtime inference systems.
- Experience shipping battery\-powered consumer electronics, wearable, neurotech, AR/VR, robotics, camera, IoT, or other embedded AI products.
Compensation Range
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$150,000 \- $200,000/year
Benefits
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- Competitive equity package
- Comprehensive medical, dental, and vision insurance
- Company size: 20\-30 people
- Unlimited PTO
- Visa sponsorship
- 3% 401k matching
Compensation Range: $150K \- $200K
Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Tacit, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tacit AI Hiring
Tacit has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $200K - $200K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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